id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8aafa2540adcf47cb8ab3e0a38d8d04ebca5dd07330d66f62f03fedde277ac4c | [
"arxiv",
"semantic_scholar"
] | Automated Deep Learning: Neural Architecture Search Is Not the End | Deep learning (DL) has proven to be a highly effective approach for developing models in diverse contexts, including visual perception, speech recognition, and machine translation. However, the end-to-end process for applying DL is not trivial. It requires grappling with problem formulation and context understanding, d... | [
"Xuanyi Dong",
"David Jacob Kedziora",
"Katarzyna Musial",
"Bogdan Gabrys"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-12-16T00:00:00 | https://arxiv.org/abs/2112.09245 | https://arxiv.org/pdf/2112.09245v3 | 2112.09245 | 10.1561/2200000119 | 31 | 3 | false | null | null | 0.3763 |
a20f6fae90749c234606fd5320bf95c24153ee2aa731c57e382fd4e9ea66d250 | [
"arxiv",
"semantic_scholar"
] | Quantum Machine Learning for Radio Astronomy | In this work we introduce a novel approach to the pulsar classification problem in time-domain radio astronomy using a Born machine, often referred to as a quantum neural network. Using a single-qubit architecture, we show that the pulsar classification problem maps well to the Bloch sphere and that comparable accuraci... | [
"Mohammad Kordzanganeh",
"Aydin Utting",
"Anna Scaife"
] | [
"quant-ph",
"astro-ph.HE",
"stat.ML"
] | [
"Physics",
"Mathematics"
] | 2021-12-05T00:00:00 | https://arxiv.org/abs/2112.02655 | https://arxiv.org/pdf/2112.02655v2 | 2112.02655 | null | 14 | 2 | false | null | null | 0.294 |
33d18fd706b48d41acf96fa7f1a5a3e48c956b4299cb4dbb14e32f12e805748e | [
"arxiv",
"semantic_scholar"
] | Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data | Statistical models are central to machine learning with broad applicability across a range of downstream tasks. The models are controlled by free parameters that are typically estimated from data by maximum-likelihood estimation or approximations thereof. However, when faced with real-world data sets many of the models... | [
"Vaidotas Simkus",
"Benjamin Rhodes",
"Michael U. Gutmann"
] | [
"cs.LG",
"stat.ME",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-11-25T00:00:00 | https://arxiv.org/abs/2111.13180 | https://arxiv.org/pdf/2111.13180v4 | 2111.13180 | null | 9 | 0 | false | null | Journal of machine learning research | 0.25 |
3047e0dfeb8002f21b2b180609ad382a38f722e8a9d4316ad83cd9fd8a61b369 | [
"arxiv",
"semantic_scholar"
] | Public Policymaking for International Agricultural Trade using Association Rules and Ensemble Machine Learning | International economics has a long history of improving our understanding of factors causing trade, and the consequences of free flow of goods and services across countries. The recent shocks to the free trade regime, especially trade disputes among major economies, as well as black swan events, such as trade wars and ... | [
"Feras A. Batarseh",
"Munisamy Gopinath",
"Anderson Monken",
"Zhengrong Gu"
] | [
"cs.LG",
"cs.AI",
"econ.GN"
] | [
"Computer Science",
"Economics"
] | 2021-11-15T00:00:00 | https://arxiv.org/abs/2111.07508 | https://arxiv.org/pdf/2111.07508v1 | 2111.07508 | 10.1016/j.mlwa.2021.100046 | 17 | 1 | false | null | arXiv.org | 0.3138 |
591fe229595d65f1d18331a4a71959db071ec792d6ed7eba5dc434d14fe199cf | [
"arxiv",
"semantic_scholar"
] | T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging | Lesion segmentation in medical imaging has been an important topic in clinical research. Researchers have proposed various detection and segmentation algorithms to address this task. Recently, deep learning-based approaches have significantly improved the performance over conventional methods. However, most state-of-th... | [
"Dong Yang",
"Andriy Myronenko",
"Xiaosong Wang",
"Ziyue Xu",
"Holger R. Roth",
"Daguang Xu"
] | [
"eess.IV",
"cs.CV",
"cs.LG"
] | [
"Computer Science",
"Engineering"
] | 2021-11-15T00:00:00 | https://arxiv.org/abs/2111.07535 | https://arxiv.org/pdf/2111.07535v1 | 2111.07535 | 10.1109/ICCV48922.2021.00393 | 28 | 1 | false | null | IEEE International Conference on Computer Vision | 0.3656 |
b280d24fe72918da98407663b7bf7fd28e01d65d8881ad33754436fc21a69581 | [
"arxiv",
"semantic_scholar"
] | A Machine Learning Approach for Recruitment Prediction in Clinical Trial Design | Significant advancements have been made in recent years to optimize patient recruitment for clinical trials, however, improved methods for patient recruitment prediction are needed to support trial site selection and to estimate appropriate enrollment timelines in the trial design stage. In this paper, using data from ... | [
"Jingshu Liu",
"Patricia J Allen",
"Luke Benz",
"Daniel Blickstein",
"Evon Okidi",
"Xiao Shi"
] | [
"cs.LG",
"stat.AP",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-11-14T00:00:00 | https://arxiv.org/abs/2111.07407 | https://arxiv.org/pdf/2111.07407v1 | 2111.07407 | null | 5 | 0 | false | null | arXiv.org | 0.1945 |
4713e842be667f84231104eda4c07d7e3734e3c5c19199867229911b9348b4b5 | [
"arxiv",
"semantic_scholar"
] | AlphaD3M: Machine Learning Pipeline Synthesis | We introduce AlphaD3M, an automatic machine learning (AutoML) system based on meta reinforcement learning using sequence models with self play. AlphaD3M is based on edit operations performed over machine learning pipeline primitives providing explainability. We compare AlphaD3M with state-of-the-art AutoML systems: Aut... | [
"Iddo Drori",
"Yamuna Krishnamurthy",
"Remi Rampin",
"Raoni de Paula Lourenco",
"Jorge Piazentin Ono",
"Kyunghyun Cho",
"Claudio Silva",
"Juliana Freire"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-11-03T00:00:00 | https://arxiv.org/abs/2111.02508 | https://arxiv.org/pdf/2111.02508v1 | 2111.02508 | null | 93 | 5 | false | null | arXiv.org | 0.4933 |
61739626d253daa51e2ed094a9056507425652d9d18ee98ca6431023ac31ef43 | [
"arxiv",
"semantic_scholar"
] | Guided Evolution for Neural Architecture Search | Neural Architecture Search (NAS) methods have been successfully applied to image tasks with excellent results. However, NAS methods are often complex and tend to converge to local minima as soon as generated architectures seem to yield good results. In this paper, we propose G-EA, a novel approach for guided evolutiona... | [
"Vasco Lopes",
"Miguel Santos",
"Bruno Degardin",
"Luís A. Alexandre"
] | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-10-28T00:00:00 | https://arxiv.org/abs/2110.15232 | https://arxiv.org/pdf/2110.15232v1 | 2110.15232 | null | 10 | 1 | false | null | arXiv.org | 0.2603 |
ce47b84b92c549bb5dd789818cc85eb88ca6cd475bc759bce7a8f1a2f9d80ee7 | [
"arxiv",
"semantic_scholar"
] | NAS-FCOS: Efficient Search for Object Detection Architectures | Neural Architecture Search (NAS) has shown great potential in effectively reducing manual effort in network design by automatically discovering optimal architectures. What is noteworthy is that as of now, object detection is less touched by NAS algorithms despite its significant importance in computer vision. To the be... | [
"Ning Wang",
"Yang Gao",
"Hao Chen",
"Peng Wang",
"Zhi Tian",
"Chunhua Shen",
"Yanning Zhang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2021-10-24T00:00:00 | https://arxiv.org/abs/2110.12423 | https://arxiv.org/pdf/2110.12423v1 | 2110.12423 | 10.1007/s11263-021-01523-2 | 24 | 1 | true | https://github.com/Lausannen/NAS-FCOS | International Journal of Computer Vision | 0.3495 |
8047c2cc627a905048ea9fa67d58b5eccc348fc78a0bdda9cbbd19805f29f6f9 | [
"arxiv",
"semantic_scholar"
] | Improving the sample-efficiency of neural architecture search with reinforcement learning | Designing complex architectures has been an essential cogwheel in the revolution deep learning has brought about in the past decade. When solving difficult problems in a datadriven manner, a well-tried approach is to take an architecture discovered by renowned deep learning scientists as a basis (e.g. Inception) and tr... | [
"Attila Nagy",
"Ábel Boros"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-10-13T00:00:00 | https://arxiv.org/abs/2110.06751 | https://arxiv.org/pdf/2110.06751v1 | 2110.06751 | null | 3 | 0 | false | null | arXiv.org | 0.1505 |
4900ccee1ede546630b4a954761a1a37ab415accc8d8c89ab39b11a3bfd4022d | [
"arxiv",
"semantic_scholar"
] | NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks | Most existing neural architecture search (NAS) benchmarks and algorithms prioritize well-studied tasks, e.g. image classification on CIFAR or ImageNet. This makes the performance of NAS approaches in more diverse areas poorly understood. In this paper, we present NAS-Bench-360, a benchmark suite to evaluate methods on ... | [
"Renbo Tu",
"Nicholas Roberts",
"Mikhail Khodak",
"Junhong Shen",
"Frederic Sala",
"Ameet Talwalkar"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2021-10-12T00:00:00 | https://arxiv.org/abs/2110.05668 | https://arxiv.org/pdf/2110.05668v6 | 2110.05668 | 10.52202/068431-0899 | 43 | 5 | false | null | Neural Information Processing Systems | 0.4109 |
b0b78809e93310b61b125b7f08e5827965f9d458682b8047f86d052c288b2903 | [
"arxiv",
"semantic_scholar"
] | Across-Task Neural Architecture Search via Meta Learning | Adequate labeled data and expensive compute resources are the prerequisites for the success of neural architecture search(NAS). It is challenging to apply NAS in meta-learning scenarios with limited compute resources and data. In this paper, an across-task neural architecture search (AT-NAS) is proposed to address the ... | [
"Jingtao Rong",
"Xinyi Yu",
"Mingyang Zhang",
"Linlin Ou"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-10-12T00:00:00 | https://arxiv.org/abs/2110.05842 | https://arxiv.org/pdf/2110.05842v1 | 2110.05842 | 10.1007/s13042-022-01678-5 | 1 | 0 | false | null | International Journal of Machine Learning and Cybernetics | 0.0753 |
f5dc40b4b1f68bc61bafe9b3ebc6dca8c0c7e099ce273f2ac4eb68f4707696a8 | [
"arxiv",
"semantic_scholar"
] | QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks | The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks. To bridge the gap, this work proposes an end-to-end learning framework named QTN-VQC, by introducing a trainable quantum tensor network (QTN) for quantum embeddi... | [
"Jun Qi",
"Chao-Han Huck Yang",
"Pin-Yu Chen"
] | [
"quant-ph",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG",
"cs.NE"
] | [
"Physics",
"Computer Science"
] | 2021-10-06T00:00:00 | https://arxiv.org/abs/2110.03861 | https://arxiv.org/pdf/2110.03861v3 | 2110.03861 | 10.1088/1402-4896/ad14d6 | 65 | 4 | false | null | Physica Scripta | 0.4549 |
f9cd7cd8fd10a0c8cbb8b01264da27163e687794d69de546c9a4f47556252955 | [
"arxiv",
"semantic_scholar"
] | L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning | Neural architecture search (NAS) has achieved remarkable results in deep neural network design. Differentiable architecture search converts the search over discrete architectures into a hyperparameter optimization problem which can be solved by gradient descent. However, questions have been raised regarding the effecti... | [
"Keith G. Mills",
"Fred X. Han",
"Mohammad Salameh",
"Seyed Saeed Changiz Rezaei",
"Linglong Kong",
"Wei Lu",
"Shuo Lian",
"Shangling Jui",
"Di Niu"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2021-09-25T00:00:00 | https://arxiv.org/abs/2109.12425 | https://arxiv.org/pdf/2109.12425v1 | 2109.12425 | 10.1145/3459637.3482360 | 13 | 1 | false | null | International Conference on Information and Knowledge Management | 0.2865 |
911493f35fdab11c6dfaa6bf450439429ea6b25aa44842e029e1a059a2b6d70f | [
"arxiv",
"semantic_scholar"
] | Assisted Learning for Organizations with Limited Imbalanced Data | In the era of big data, many big organizations are integrating machine learning into their work pipelines to facilitate data analysis. However, the performance of their trained models is often restricted by limited and imbalanced data available to them. In this work, we develop an assisted learning framework for assist... | [
"Cheng Chen",
"Jiaying Zhou",
"Jie Ding",
"Yi Zhou"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-09-20T00:00:00 | https://arxiv.org/abs/2109.09307 | https://arxiv.org/pdf/2109.09307v4 | 2109.09307 | null | 3 | 0 | false | null | C. Chen, J. Zhou, J. Ding, and Y. Zhou, "Assisted Learning for Organizations with Limited Imbalanced Data," Transactions on Machine Learning Research (TMLR), 2023 | 0.1505 |
6adecb4b50af94373e055c034207f22b4a4d639fef2fd75841f4fdc6bf740951 | [
"arxiv",
"semantic_scholar"
] | DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture | Automated machine learning (AutoML) usually involves several crucial components, such as Data Augmentation (DA) policy, Hyper-Parameter Optimization (HPO), and Neural Architecture Search (NAS). Although many strategies have been developed for automating these components in separation, joint optimization of these compon... | [
"Kaichen Zhou",
"Lanqing Hong",
"Shoukang Hu",
"Fengwei Zhou",
"Binxin Ru",
"Jiashi Feng",
"Zhenguo Li"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2021-09-13T00:00:00 | https://arxiv.org/abs/2109.05765 | https://arxiv.org/pdf/2109.05765v2 | 2109.05765 | null | 12 | 1 | false | null | Transactions on Machine Learning Research 2022 | 0.2785 |
99db617dc1eb7cf3d498bc9baf0da8fb2ecf8cfb77958f0628254de9dcc9b5c9 | [
"arxiv",
"semantic_scholar"
] | Estimation of Corporate Greenhouse Gas Emissions via Machine Learning | As an important step to fulfill the Paris Agreement and achieve net-zero emissions by 2050, the European Commission adopted the most ambitious package of climate impact measures in April 2021 to improve the flow of capital towards sustainable activities. For these and other international measures to be successful, reli... | [
"You Han",
"Achintya Gopal",
"Liwen Ouyang",
"Aaron Key"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-09-09T00:00:00 | https://arxiv.org/abs/2109.04318 | https://arxiv.org/pdf/2109.04318v1 | 2109.04318 | null | 12 | 0 | false | null | arXiv.org | 0.2785 |
f58003fb608c43bc3418c61b652365c0b8db5bd79ba643da59d7dcb0648cf3d2 | [
"arxiv",
"semantic_scholar"
] | NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization | Recent advances on Out-of-Distribution (OoD) generalization reveal the robustness of deep learning models against distribution shifts. However, existing works focus on OoD algorithms, such as invariant risk minimization, domain generalization, or stable learning, without considering the influence of deep model architec... | [
"Haoyue Bai",
"Fengwei Zhou",
"Lanqing Hong",
"Nanyang Ye",
"S. -H. Gary Chan",
"Zhenguo Li"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-09-05T00:00:00 | https://arxiv.org/abs/2109.02038 | https://arxiv.org/pdf/2109.02038v1 | 2109.02038 | 10.1109/ICCV48922.2021.00821 | 47 | 4 | false | null | IEEE International Conference on Computer Vision | 0.4203 |
36ccd62b0841d089c040fadb6b88d0fc366b3ff75cb9be32a37cca7baefefdf5 | [
"arxiv",
"semantic_scholar"
] | Ovarian Cancer Prediction from Ovarian Cysts Based on TVUS Using Machine Learning Algorithms | Ovarian Cancer (OC) is type of female reproductive malignancy which can be found among young girls and mostly the women in their fertile or reproductive. There are few number of cysts are dangerous and may it cause cancer. So, it is very important to predict and it can be from different types of screening are used for ... | [
"Laboni Akter",
"Nasrin Akhter"
] | [
"cs.LG",
"eess.IV",
"q-bio.BM",
"stat.ML"
] | [
"Computer Science",
"Engineering",
"Biology",
"Mathematics"
] | 2021-08-30T00:00:00 | https://arxiv.org/abs/2108.13387 | https://arxiv.org/pdf/2108.13387v1 | 2108.13387 | 10.1007/978-981-16-6636-0_5 | 15 | 1 | false | null | null | 0.301 |
88936db88bc093011254e6a4fb347134c6bf0c6e5f55b9dbebfa9415c4940886 | [
"arxiv",
"semantic_scholar"
] | Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift | Recently proposed neural architecture search (NAS) methods co-train billions of architectures in a supernet and estimate their potential accuracy using the network weights detached from the supernet. However, the ranking correlation between the architectures' predicted accuracy and their actual capability is incorrect,... | [
"Jiefeng Peng",
"Jiqi Zhang",
"Changlin Li",
"Guangrun Wang",
"Xiaodan Liang",
"Liang Lin"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2021-08-22T00:00:00 | https://arxiv.org/abs/2108.09671 | https://arxiv.org/pdf/2108.09671v1 | 2108.09671 | 10.1109/ICCV48922.2021.01213 | 21 | 2 | true | https://github.com/Ernie1/Pi-NAS | IEEE International Conference on Computer Vision | 0.3356 |
f8e47f2b2e7a69711e70bb067676070a45c418b5555437328a2908126fda4dc0 | [
"arxiv",
"semantic_scholar"
] | Teaching Uncertainty Quantification in Machine Learning through Use Cases | Uncertainty in machine learning is not generally taught as general knowledge in Machine Learning course curricula. In this paper we propose a short curriculum for a course about uncertainty in machine learning, and complement the course with a selection of use cases, aimed to trigger discussion and let students play wi... | [
"Matias Valdenegro-Toro"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-08-19T00:00:00 | https://arxiv.org/abs/2108.08712 | https://arxiv.org/pdf/2108.08712v1 | 2108.08712 | null | 2 | 0 | false | null | null | 0.1193 |
735e095e9aeb1395f4f71f03babc316187f903bd650ee878539a874f3cfd1380 | [
"arxiv",
"semantic_scholar"
] | InfoGram and Admissible Machine Learning | We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under the regulatory constraints. This has led to great interest in developing fair, transparent and trustworthy ML methods. The purpose of this articl... | [
"Subhadeep Mukhopadhyay"
] | [
"stat.ML",
"cs.AI",
"cs.LG",
"econ.EM"
] | [
"Computer Science",
"Mathematics",
"Economics"
] | 2021-08-17T00:00:00 | https://arxiv.org/abs/2108.07380 | https://arxiv.org/pdf/2108.07380v2 | 2108.07380 | 10.1007/s10994-021-06121-4 | 9 | 0 | false | null | Machine-mediated learning | 0.25 |
8e41f0015922db702a1dde30b822efc5374070ea4367ad187c6125b38812bddf | [
"arxiv",
"semantic_scholar"
] | BN-NAS: Neural Architecture Search with Batch Normalization | We present BN-NAS, neural architecture search with Batch Normalization (BN-NAS), to accelerate neural architecture search (NAS). BN-NAS can significantly reduce the time required by model training and evaluation in NAS. Specifically, for fast evaluation, we propose a BN-based indicator for predicting subnet performance... | [
"Boyu Chen",
"Peixia Li",
"Baopu Li",
"Chen Lin",
"Chuming Li",
"Ming Sun",
"Junjie Yan",
"Wanli Ouyang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2021-08-16T00:00:00 | https://arxiv.org/abs/2108.07375 | https://arxiv.org/pdf/2108.07375v1 | 2108.07375 | 10.1109/ICCV48922.2021.00037 | 35 | 5 | false | null | IEEE International Conference on Computer Vision | 0.3891 |
94371f8219ab08ef2c2196117a67293549fe43870b48ae7b032b4e7a39b93eb8 | [
"arxiv",
"semantic_scholar"
] | FOX-NAS: Fast, On-device and Explainable Neural Architecture Search | Neural architecture search can discover neural networks with good performance, and One-Shot approaches are prevalent. One-Shot approaches typically require a supernet with weight sharing and predictors that predict the performance of architecture. However, the previous methods take much time to generate performance pre... | [
"Chia-Hsiang Liu",
"Yu-Shin Han",
"Yuan-Yao Sung",
"Yi Lee",
"Hung-Yueh Chiang",
"Kai-Chiang Wu"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2021-08-14T00:00:00 | https://arxiv.org/abs/2108.08189 | https://arxiv.org/pdf/2108.08189v1 | 2108.08189 | 10.1109/ICCVW54120.2021.00093 | 15 | 2 | true | https://github.com/great8nctu/FOX-NAS | null | 0.301 |
9e0c296532ca6248cb9761f41f862a67a1c37d107ac2df8143aa63af5b0866a3 | [
"arxiv",
"semantic_scholar"
] | Efficient Neural Architecture Search with Performance Prediction | Neural networks are powerful models that have a remarkable ability to extract patterns that are too complex to be noticed by humans or other machine learning models. Neural networks are the first class of models that can train end-to-end systems with large learning capacities. However, we still have the difficult chall... | [
"Ibrahim Alshubaily"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-08-04T00:00:00 | https://arxiv.org/abs/2108.01854 | https://arxiv.org/pdf/2108.01854v1 | 2108.01854 | null | 3 | 0 | false | null | null | 0.1505 |
6f7b591b1d780226545104c107db369c78eaae60bfe33c74596b330493ad2b38 | [
"arxiv",
"semantic_scholar"
] | Automated Learning Rate Scheduler for Large-batch Training | Large-batch training has been essential in leveraging large-scale datasets and models in deep learning. While it is computationally beneficial to use large batch sizes, it often requires a specially designed learning rate (LR) schedule to achieve a comparable level of performance as in smaller batch training. Especiall... | [
"Chiheon Kim",
"Saehoon Kim",
"Jongmin Kim",
"Donghoon Lee",
"Sungwoong Kim"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-07-13T00:00:00 | https://arxiv.org/abs/2107.05855 | https://arxiv.org/pdf/2107.05855v1 | 2107.05855 | null | 28 | 5 | false | null | arXiv.org | 0.3891 |
89c331e02bf67fb47aa09d1c71fe2f9e29ade3d427b66e71f36c5a2acf2a2064 | [
"arxiv",
"semantic_scholar"
] | Core-set Sampling for Efficient Neural Architecture Search | Neural architecture search (NAS), an important branch of automatic machine learning, has become an effective approach to automate the design of deep learning models. However, the major issue in NAS is how to reduce the large search time imposed by the heavy computational burden. While most recent approaches focus on pr... | [
"Jae-hun Shim",
"Kyeongbo Kong",
"Suk-Ju Kang"
] | [
"cs.LG",
"cs.NE"
] | [
"Computer Science"
] | 2021-07-08T00:00:00 | https://arxiv.org/abs/2107.06869 | https://arxiv.org/pdf/2107.06869v1 | 2107.06869 | null | 32 | 3 | false | null | arXiv.org | 0.3796 |
96831650473938045b2bb97bf70d143a7a2696506da667980858772ef2783209 | [
"arxiv",
"semantic_scholar"
] | Asymptotics of Network Embeddings Learned via Subsampling | Network data are ubiquitous in modern machine learning, with tasks of interest including node classification, node clustering and link prediction. A frequent approach begins by learning an Euclidean embedding of the network, to which algorithms developed for vector-valued data are applied. For large networks, embedding... | [
"Andrew Davison",
"Morgane Austern"
] | [
"stat.ML",
"cs.LG",
"math.ST"
] | [
"Mathematics",
"Computer Science"
] | 2021-07-06T00:00:00 | https://arxiv.org/abs/2107.02363 | https://arxiv.org/pdf/2107.02363v4 | 2107.02363 | null | 14 | 5 | false | null | arXiv.org | 0.3891 |
0abac6e2f5569df2912a5f43c91f8ed2af4782ddb00e1ed639461e3e0fbd0703 | [
"arxiv",
"semantic_scholar"
] | Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets | Despite the success of recent Neural Architecture Search (NAS) methods on various tasks which have shown to output networks that largely outperform human-designed networks, conventional NAS methods have mostly tackled the optimization of searching for the network architecture for a single task (dataset), which does not... | [
"Hayeon Lee",
"Eunyoung Hyung",
"Sung Ju Hwang"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2021-07-02T00:00:00 | https://arxiv.org/abs/2107.00860 | https://arxiv.org/pdf/2107.00860v1 | 2107.00860 | null | 56 | 8 | true | https://github.com/HayeonLee/MetaD2A | International Conference on Learning Representations | 0.4771 |
db426d1ebb9ac9c7ad66744d5cbf980449541115fd5905cadf565030e2d26192 | [
"arxiv",
"semantic_scholar"
] | Poisoning the Search Space in Neural Architecture Search | Deep learning has proven to be a highly effective problem-solving tool for object detection and image segmentation across various domains such as healthcare and autonomous driving. At the heart of this performance lies neural architecture design which relies heavily on domain knowledge and prior experience on the resea... | [
"Robert Wu",
"Nayan Saxena",
"Rohan Jain"
] | [
"cs.LG",
"cs.CR",
"cs.NE",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-06-28T00:00:00 | https://arxiv.org/abs/2106.14406 | https://arxiv.org/pdf/2106.14406v1 | 2106.14406 | null | 2 | 0 | false | null | arXiv.org | 0.1193 |
7b376736d5be7b3896f31acc65140c9b7453b0c484fa3faf50bbfebf39bfb724 | [
"arxiv",
"semantic_scholar"
] | Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization | When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device latency and model accuracy. In this work, we leverage recent methodological advances in Bayesian optimization over high-dimensional search sp... | [
"David Eriksson",
"Pierce I-Jen Chuang",
"Samuel Daulton",
"Peng Xia",
"Akshat Shrivastava",
"Arun Babu",
"Shicong Zhao",
"Ahmed Aly",
"Ganesh Venkatesh",
"Maximilian Balandat"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-06-22T00:00:00 | https://arxiv.org/abs/2106.11890 | https://arxiv.org/pdf/2106.11890v2 | 2106.11890 | null | 21 | 0 | false | null | arXiv.org | 0.3356 |
445d6c0b1d7c968cf561113ee5bae2aa99bf5daba35d0c39a0a3d3fdc7f6fd59 | [
"arxiv",
"semantic_scholar"
] | RHNAS: Realizable Hardware and Neural Architecture Search | The rapidly evolving field of Artificial Intelligence necessitates automated approaches to co-design neural network architecture and neural accelerators to maximize system efficiency and address productivity challenges. To enable joint optimization of this vast space, there has been growing interest in differentiable N... | [
"Yash Akhauri",
"Adithya Niranjan",
"J. Pablo Muñoz",
"Suvadeep Banerjee",
"Abhijit Davare",
"Pasquale Cocchini",
"Anton A. Sorokin",
"Ravi Iyer",
"Nilesh Jain"
] | [
"cs.LG",
"cs.AR",
"cs.NE"
] | [
"Computer Science"
] | 2021-06-17T00:00:00 | https://arxiv.org/abs/2106.09180 | https://arxiv.org/pdf/2106.09180v1 | 2106.09180 | null | 3 | 0 | false | null | arXiv.org | 0.1505 |
047c3732d5548586bde01e86789f3e72f788409136ff6b57c2c99c25fd62af41 | [
"arxiv",
"semantic_scholar"
] | Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions | Backdoor attacks inject poisoning samples during training, with the goal of forcing a machine learning model to output an attacker-chosen class when presented a specific trigger at test time. Although backdoor attacks have been demonstrated in a variety of settings and against different models, the factors affecting th... | [
"Antonio Emanuele Cinà",
"Kathrin Grosse",
"Sebastiano Vascon",
"Ambra Demontis",
"Battista Biggio",
"Fabio Roli",
"Marcello Pelillo"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2021-06-14T00:00:00 | https://arxiv.org/abs/2106.07214 | https://arxiv.org/pdf/2106.07214v4 | 2106.07214 | 10.1007/s13042-024-02363-5 | 11 | 0 | false | null | International Journal of Machine Learning and Cybernetics | 0.2698 |
7d5764a75667973674c73b35e785eaf459191a7574b4c5c205381eb97e2a40ce | [
"arxiv",
"semantic_scholar"
] | HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers | High-resolution representations (HR) are essential for dense prediction tasks such as segmentation, detection, and pose estimation. Learning HR representations is typically ignored in previous Neural Architecture Search (NAS) methods that focus on image classification. This work proposes a novel NAS method, called HR-N... | [
"Mingyu Ding",
"Xiaochen Lian",
"Linjie Yang",
"Peng Wang",
"Xiaojie Jin",
"Zhiwu Lu",
"Ping Luo"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2021-06-11T00:00:00 | https://arxiv.org/abs/2106.06560 | https://arxiv.org/pdf/2106.06560v1 | 2106.06560 | 10.1109/CVPR46437.2021.00300 | 76 | 11 | true | https://github.com/dingmyu/HR-NAS | Computer Vision and Pattern Recognition | 0.5396 |
4aec8eb063af4ebfe420d695e308dc6e077c4bea65410502b551edd0ac713e76 | [
"arxiv",
"semantic_scholar"
] | Differentiable Architecture Search for Reinforcement Learning | In this paper, we investigate the fundamental question: To what extent are gradient-based neural architecture search (NAS) techniques applicable to RL? Using the original DARTS as a convenient baseline, we discover that the discrete architectures found can achieve up to 250% performance compared to manual architecture ... | [
"Yingjie Miao",
"Xingyou Song",
"John D. Co-Reyes",
"Daiyi Peng",
"Summer Yue",
"Eugene Brevdo",
"Aleksandra Faust"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2021-06-04T00:00:00 | https://arxiv.org/abs/2106.02229 | https://arxiv.org/pdf/2106.02229v4 | 2106.02229 | null | 7 | 0 | true | https://github.com/google/brain_autorl/tree/main/rl_darts | null | 0.2258 |
67e576be3619b17f9686218e13092f85762816d29c17a3f70bf03782c07752bd | [
"arxiv",
"semantic_scholar"
] | Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight | Transfer learning can boost the performance on the targettask by leveraging the knowledge of the source domain. Recent worksin neural architecture search (NAS), especially one-shot NAS, can aidtransfer learning by establishing sufficient network search space. How-ever, existing NAS methods tend to approximate huge sear... | [
"Ming Sun",
"Haoxuan Dou",
"Junjie Yan"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2021-05-19T00:00:00 | https://arxiv.org/abs/2105.08994 | https://arxiv.org/pdf/2105.08994v1 | 2105.08994 | 10.1007/978-3-030-58601-0_28 | 3 | 0 | false | null | European Conference on Computer Vision | 0.1505 |
bf225227b6517d8e81c2148c426378ca5fa1b79802329ca362bf0aa5125a244c | [
"arxiv",
"semantic_scholar"
] | Mill.jl and JsonGrinder.jl: automated differentiable feature extraction for learning from raw JSON data | Learning from raw data input, thus limiting the need for manual feature engineering, is one of the key components of many successful applications of machine learning methods. While machine learning problems are often formulated on data that naturally translate into a vector representation suitable for classifiers, ther... | [
"Simon Mandlik",
"Matej Racinsky",
"Viliam Lisy",
"Tomas Pevny"
] | [
"stat.ML",
"cs.LG",
"cs.MS"
] | [
"Computer Science",
"Mathematics"
] | 2021-05-19T00:00:00 | https://arxiv.org/abs/2105.09107 | https://arxiv.org/pdf/2105.09107v1 | 2105.09107 | null | 2 | 0 | false | null | arXiv.org | 0.1193 |
4e634da4dd27779b59a01908f80acb5471be27d068b0f361477e5dceb7e95d3f | [
"arxiv",
"semantic_scholar"
] | Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics | Democratization of machine learning requires architectures that automatically adapt to new problems. Neural Differential Equations (NDEs) have emerged as a popular modeling framework by removing the need for ML practitioners to choose the number of layers in a recurrent model. While we can control the computational cos... | [
"Avik Pal",
"Yingbo Ma",
"Viral Shah",
"Christopher Rackauckas"
] | [
"cs.LG",
"math.NA"
] | [
"Computer Science",
"Mathematics"
] | 2021-05-09T00:00:00 | https://arxiv.org/abs/2105.03918 | https://arxiv.org/pdf/2105.03918v2 | 2105.03918 | null | 44 | 1 | false | null | International Conference on Machine Learning | 0.4133 |
8e04cf63b47be2661ef950509631d0da86d68453bc77209f3ff688f5901d3494 | [
"arxiv",
"semantic_scholar"
] | Making Differentiable Architecture Search less local | Neural architecture search (NAS) is a recent methodology for automating the design of neural network architectures. Differentiable neural architecture search (DARTS) is a promising NAS approach that dramatically increases search efficiency. However, it has been shown to suffer from performance collapse, where the searc... | [
"Erik Bodin",
"Federico Tomasi",
"Zhenwen Dai"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-04-21T00:00:00 | https://arxiv.org/abs/2104.10450 | https://arxiv.org/pdf/2104.10450v1 | 2104.10450 | null | 0 | 0 | false | null | arXiv.org | 0 |
9a6c04f61b0f83807c9e23096a30752049b7db7ac15afb9629ec9f08e0941d91 | [
"arxiv",
"semantic_scholar"
] | BM-NAS: Bilevel Multimodal Neural Architecture Search | Deep neural networks (DNNs) have shown superior performances on various multimodal learning problems. However, it often requires huge efforts to adapt DNNs to individual multimodal tasks by manually engineering unimodal features and designing multimodal feature fusion strategies. This paper proposes Bilevel Multimodal ... | [
"Yihang Yin",
"Siyu Huang",
"Xiang Zhang"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2021-04-19T00:00:00 | https://arxiv.org/abs/2104.09379 | https://arxiv.org/pdf/2104.09379v2 | 2104.09379 | 10.1609/aaai.v36i8.20872 | 36 | 7 | false | null | AAAI Conference on Artificial Intelligence | 0.4515 |
896de9e4a85eb0ad132dc08879b8fc1d8dd2f00757c7cbfa8bd204daaa92072c | [
"arxiv",
"semantic_scholar"
] | Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems | As real-world images come in varying sizes, the machine learning model is part of a larger system that includes an upstream image scaling algorithm. In this paper, we investigate the interplay between vulnerabilities of the image scaling procedure and machine learning models in the decision-based black-box setting. We ... | [
"Yue Gao",
"Ilia Shumailov",
"Kassem Fawaz"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2021-04-18T00:00:00 | https://arxiv.org/abs/2104.08690 | https://arxiv.org/pdf/2104.08690v3 | 2104.08690 | null | 12 | 1 | false | null | International Conference on Machine Learning | 0.2785 |
ae70be27d4c4ae7ad6dc9d3b672a727ca0383ecb65ee20028dcf247aa6ec5bb1 | [
"arxiv",
"semantic_scholar"
] | AutoGL: A Library for Automated Graph Learning | Recent years have witnessed an upsurge in research interests and applications of machine learning on graphs. However, manually designing the optimal machine learning algorithms for different graph datasets and tasks is inflexible, labor-intensive, and requires expert knowledge, limiting its adaptivity and applicability... | [
"Ziwei Zhang",
"Yijian Qin",
"Zeyang Zhang",
"Chaoyu Guan",
"Jie Cai",
"Heng Chang",
"Jiyan Jiang",
"Haoyang Li",
"Zixin Sun",
"Beini Xie",
"Yang Yao",
"Yipeng Zhang",
"Xin Wang",
"Wenwu Zhu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-04-11T00:00:00 | https://arxiv.org/abs/2104.04987 | https://arxiv.org/pdf/2104.04987v4 | 2104.04987 | null | 33 | 0 | true | https://github.com/THUMNLab/AutoGL | arXiv.org | 0.3829 |
a133e31ed64805d35056b7183842c1ca0a2449317b6d4dc3e475c0ef3587958f | [
"arxiv",
"semantic_scholar"
] | DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python | DoubleML is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. (2018) for a variety of causal models. It contains functionalities for valid statistical inference on causal parameters when the estimation of nuisance parameters is based on machine learning methods. The... | [
"Philipp Bach",
"Victor Chernozhukov",
"Malte S. Kurz",
"Martin Spindler"
] | [
"stat.ML",
"cs.LG",
"econ.EM"
] | [
"Mathematics",
"Computer Science",
"Economics"
] | 2021-04-07T00:00:00 | https://arxiv.org/abs/2104.03220 | https://arxiv.org/pdf/2104.03220v2 | 2104.03220 | null | 64 | 6 | true | https://github.com/DoubleML/doubleml-for-py | Journal of machine learning research | 0.4532 |
99199c44f3f01aa8d5b95d2ab159bbe4b8d67e562cecb21fc5233b73a19eac4a | [
"arxiv",
"semantic_scholar"
] | Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition | Current state-of-the-art visual recognition systems usually rely on the following pipeline: (a) pretraining a neural network on a large-scale dataset (e.g., ImageNet) and (b) finetuning the network weights on a smaller, task-specific dataset. Such a pipeline assumes the sole weight adaptation is able to transfer the ne... | [
"Guangrun Wang",
"Liang Lin",
"Rongcong Chen",
"Guangcong Wang",
"Jiqi Zhang"
] | [
"cs.CV"
] | [
"Computer Science",
"Medicine"
] | 2021-03-31T00:00:00 | https://arxiv.org/abs/2103.16889 | https://arxiv.org/pdf/2103.16889v1 | 2103.16889 | 10.1109/TNNLS.2021.3070605 | 13 | 0 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.2865 |
cfdd7ee8dfe416949ae9b95586fea309c0e4b49db7c1bbe2bca67322e42dd87c | [
"arxiv",
"semantic_scholar"
] | Prioritized Architecture Sampling with Monto-Carlo Tree Search | One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once. However, current methods select each operation independently without considering previous layers. Besides, the historical information obtained... | [
"Xiu Su",
"Tao Huang",
"Yanxi Li",
"Shan You",
"Fei Wang",
"Chen Qian",
"Changshui Zhang",
"Chang Xu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2021-03-22T00:00:00 | https://arxiv.org/abs/2103.11922 | https://arxiv.org/pdf/2103.11922v1 | 2103.11922 | 10.1109/CVPR46437.2021.01082 | 56 | 9 | true | https://github.com/xiusu/NAS-Bench-Macro} | Computer Vision and Pattern Recognition | 0.5 |
0435e120ec13a23f99eb3fa1bf6fffccdf2c5e60d7a111184739722f92218871 | [
"arxiv",
"semantic_scholar"
] | HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark | HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous attention by automating the design of DNNs deployed in more resource-constrained daily life devices. Despite its promising performance, developing optimal HW-NAS solutions can be prohibitively challenging as it requires cross-disciplinary... | [
"Chaojian Li",
"Zhongzhi Yu",
"Yonggan Fu",
"Yongan Zhang",
"Yang Zhao",
"Haoran You",
"Qixuan Yu",
"Yue Wang",
"Yingyan Celine Lin"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-03-19T00:00:00 | https://arxiv.org/abs/2103.10584 | https://arxiv.org/pdf/2103.10584v2 | 2103.10584 | null | 132 | 21 | true | https://github.com/RICE-EIC/HW-NAS-Bench | International Conference on Learning Representations | 0.6712 |
bd04e2298c67742d537c28379e530c85665546791689fcd9f3754198ab3597b9 | [
"arxiv",
"semantic_scholar"
] | Naive Automated Machine Learning -- A Late Baseline for AutoML | Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on some given dataset. AutoML has received enormous attention in the last decade and has been addressed with sophisticated black-box optimization techniques such as Bayesian Optimization, Gr... | [
"Felix Mohr",
"Marcel Wever"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-03-18T00:00:00 | https://arxiv.org/abs/2103.10496 | https://arxiv.org/pdf/2103.10496v1 | 2103.10496 | null | 0 | 0 | false | null | arXiv.org | 0 |
c4f66c5c1cd6f56a68b597626b55c183648b49c7a7f094865bb84fbbe5600475 | [
"arxiv",
"semantic_scholar"
] | NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition | In the field of complex action recognition in videos, the quality of the designed model plays a crucial role in the final performance. However, artificially designed network structures often rely heavily on the researchers' knowledge and experience. Accordingly, because of the automated design of its network structure,... | [
"Pengzhen Ren",
"Gang Xiao",
"Xiaojun Chang",
"Yun Xiao",
"Zhihui Li",
"Xiaojiang Chen"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2021-03-17T00:00:00 | https://arxiv.org/abs/2104.01110 | https://arxiv.org/pdf/2104.01110v1 | 2104.01110 | null | 7 | 0 | false | null | arXiv.org | 0.2258 |
e1be01f42d4301d634515c9036c373128a9c0c1fa6557c4644204233cbcade2f | [
"arxiv",
"semantic_scholar"
] | Pretraining Neural Architecture Search Controllers with Locality-based Self-Supervised Learning | Neural architecture search (NAS) has fostered various fields of machine learning. Despite its prominent dedications, many have criticized the intrinsic limitations of high computational cost. We aim to ameliorate this by proposing a pretraining scheme that can be generally applied to controller-based NAS. Our method, l... | [
"Kwanghee Choi",
"Minyoung Choe",
"Hyelee Lee"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-03-15T00:00:00 | https://arxiv.org/abs/2103.08157 | https://arxiv.org/pdf/2103.08157v1 | 2103.08157 | null | 1 | 0 | true | https://github.com/Multi-Objective-NAS/self-supervised-nas} | arXiv.org | 0.0753 |
3e6e194ab04d772f7c9a4b361d812ce0f755d0c92dfa14b49eea8c1f8c98ae8b | [
"arxiv",
"semantic_scholar"
] | Continual Learning for Recurrent Neural Networks: an Empirical Evaluation | Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with recurrent neural networks could pave the way to a large number of applications where incoming data is non stationary, like natural langu... | [
"Andrea Cossu",
"Antonio Carta",
"Vincenzo Lomonaco",
"Davide Bacciu"
] | [
"cs.LG",
"cs.AI"
] | [
"Medicine",
"Computer Science"
] | 2021-03-12T00:00:00 | https://arxiv.org/abs/2103.07492 | https://arxiv.org/pdf/2103.07492v4 | 2103.07492 | 10.1016/j.neunet.2021.07.021 | 125 | 5 | false | null | Neural Networks | 0.5251 |
a5275ff3afa1d5b4a67f806269d6f13af102d30695a4cad39697099942e78886 | [
"arxiv",
"semantic_scholar"
] | Neural Architecture Search based on Cartesian Genetic Programming Coding Method | Neural architecture search (NAS) is a hot topic in the field of automated machine learning and outperforms humans in designing neural architectures on quite a few machine learning tasks. Motivated by the natural representation form of neural networks by the Cartesian genetic programming (CGP), we propose an evolutionar... | [
"Xuan Wu",
"Linhan Jia",
"Xiuyi Zhang",
"Liang Chen",
"Yanchun Liang",
"You Zhou",
"Chunguo Wu"
] | [
"cs.NE"
] | [
"Computer Science"
] | 2021-03-12T00:00:00 | https://arxiv.org/abs/2103.07173 | https://arxiv.org/pdf/2103.07173v5 | 2103.07173 | null | 0 | 0 | false | null | null | 0 |
ac735a692ae97c5f59a14e0fec5b1509ec9a5f451b387e12c639136bd68895df | [
"arxiv",
"semantic_scholar"
] | An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance | With the enrichment of smartphones, driving distractions caused by phone usages have become a threat to driving safety. A promising way to mitigate driving distractions is to detect them and give real-time safety warnings. However, existing detection algorithms face two major challenges, low user acceptance caused by i... | [
"Chen Chai",
"Juanwu Lu",
"Xuan Jiang",
"Xiupeng Shi",
"Zeng Zeng"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-03-10T00:00:00 | https://arxiv.org/abs/2103.08311 | https://arxiv.org/pdf/2103.08311v1 | 2103.08311 | null | 5 | 1 | false | null | arXiv.org | 0.1945 |
f5d0baeb33afb7e110362abac650349cac648bbcecfa439c346b9c202aeaccbb | [
"arxiv",
"semantic_scholar"
] | Machine Learning using Stata/Python | We present two related Stata modules, r_ml_stata and c_ml_stata, for fitting popular Machine Learning (ML) methods both in regression and classification settings. Using the recent Stata/Python integration platform (sfi) of Stata 16, these commands provide hyper-parameters' optimal tuning via K-fold cross-validation usi... | [
"Giovanni Cerulli"
] | [
"stat.CO",
"cs.LG",
"cs.MS"
] | [
"Mathematics",
"Computer Science"
] | 2021-03-03T00:00:00 | https://arxiv.org/abs/2103.03122 | https://arxiv.org/pdf/2103.03122v1 | 2103.03122 | 10.1177/1536867X221140944 | 12 | 0 | false | null | The Stata Journal | 0.2785 |
7448c6e6f2a982982a2391600bdeef04e3b3c316aeec19253e57ae5037f4a2a2 | [
"arxiv",
"semantic_scholar"
] | Automated Machine Learning on Graphs: A Survey | Machine learning on graphs has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. ... | [
"Ziwei Zhang",
"Xin Wang",
"Wenwu Zhu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-03-01T00:00:00 | https://arxiv.org/abs/2103.00742 | https://arxiv.org/pdf/2103.00742v4 | 2103.00742 | 10.24963/ijcai.2021/637 | 95 | 2 | true | null | International Joint Conference on Artificial Intelligence | 0.4956 |
7be038d341654f818153165da722b66669ad45a0c6d3c49020a9fdf3facb51e6 | [
"arxiv",
"semantic_scholar"
] | HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search | Realistic use of neural networks often requires adhering to multiple constraints on latency, energy and memory among others. A popular approach to find fitting networks is through constrained Neural Architecture Search (NAS), however, previous methods enforce the constraint only softly. Therefore, the resulting network... | [
"Niv Nayman",
"Yonathan Aflalo",
"Asaf Noy",
"Lihi Zelnik-Manor"
] | [
"cs.LG",
"cs.AI",
"cs.CV",
"math.OC",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-02-23T00:00:00 | https://arxiv.org/abs/2102.11646 | https://arxiv.org/pdf/2102.11646v1 | 2102.11646 | null | 42 | 2 | true | https://github.com/Alibaba-MIIL/HardCoReNAS | International Conference on Machine Learning | 0.4084 |
06795a2400fedf0f41041d75029d76b8e6afa70a5c2278f732f84443a321fd2b | [
"arxiv",
"semantic_scholar"
] | EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search | Neural Architecture Search (NAS) has shown excellent results in designing architectures for computer vision problems. NAS alleviates the need for human-defined settings by automating architecture design and engineering. However, NAS methods tend to be slow, as they require large amounts of GPU computation. This bottlen... | [
"Vasco Lopes",
"Saeid Alirezazadeh",
"Luís A. Alexandre"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2021-02-16T00:00:00 | https://arxiv.org/abs/2102.08099 | https://arxiv.org/pdf/2102.08099v1 | 2102.08099 | 10.1007/978-3-030-86383-8_44 | 51 | 4 | false | null | International Conference on Artificial Neural Networks | 0.429 |
df6444ec0cf2f8a70e376999dc82ba0747df01282c2a604424c991abcd380c16 | [
"arxiv",
"semantic_scholar"
] | Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modelling and Automation | This paper illustrates an application of machine learning (ML) within a complex system that performs grade estimation. In surface mining, assay measurements taken from production drilling often provide useful information that allows initially inaccurate surfaces created using sparse exploration data to be revised and s... | [
"Raymond Leung",
"Mehala Balamurali",
"Alexander Lowe"
] | [
"physics.geo-ph",
"cs.CE",
"cs.LG"
] | [
"Physics",
"Computer Science"
] | 2021-02-15T00:00:00 | https://arxiv.org/abs/2103.03923 | https://arxiv.org/pdf/2103.03923v3 | 2103.03923 | 10.1007/s11004-021-09967-5 | 8 | 1 | false | null | Mathematical Geosciences | 0.2386 |
aa1e1d90aa6827aa6b439d3d3e2899e648c7f1fb3ee25a1d1d62c8febc7d58e4 | [
"arxiv",
"semantic_scholar"
] | Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification | The COVID-19 pandemic has threatened global health. Many studies have applied deep convolutional neural networks (CNN) to recognize COVID-19 based on chest 3D computed tomography (CT). Recent works show that no model generalizes well across CT datasets from different countries, and manually designing models for specifi... | [
"Xin He",
"Guohao Ying",
"Jiyong Zhang",
"Xiaowen Chu"
] | [
"eess.IV",
"cs.CV"
] | [
"Engineering",
"Computer Science"
] | 2021-01-26T00:00:00 | https://arxiv.org/abs/2101.10667 | https://arxiv.org/pdf/2101.10667v2 | 2101.10667 | 10.1007/978-3-031-16431-6_53 | 10 | 0 | false | null | International Conference on Medical Image Computing and Computer-Assisted Intervention | 0.2603 |
6dacb94751580e2aca1b30a9ab3686e42f388189633675b1e87517c06cc00e6f | [
"arxiv",
"semantic_scholar"
] | PyGlove: Symbolic Programming for Automated Machine Learning | Neural networks are sensitive to hyper-parameter and architecture choices. Automated Machine Learning (AutoML) is a promising paradigm for automating these choices. Current ML software libraries, however, are quite limited in handling the dynamic interactions among the components of AutoML. For example, efficientNAS al... | [
"Daiyi Peng",
"Xuanyi Dong",
"Esteban Real",
"Mingxing Tan",
"Yifeng Lu",
"Hanxiao Liu",
"Gabriel Bender",
"Adam Kraft",
"Chen Liang",
"Quoc V. Le"
] | [
"cs.LG",
"cs.PL"
] | [
"Computer Science"
] | 2021-01-21T00:00:00 | https://arxiv.org/abs/2101.08809 | https://arxiv.org/pdf/2101.08809v1 | 2101.08809 | null | 35 | 2 | false | null | Neural Information Processing Systems | 0.3891 |
2a74d9176af2950bb41db8b07d0e483a5d4d371d5362320f5d42d1bda994b3f1 | [
"arxiv",
"semantic_scholar"
] | Deep Cox Mixtures for Survival Regression | Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up. Such problems come up frequently in medical applications, making survival analysis a key endeavor in biostatistics and ... | [
"Chirag Nagpal",
"Steve Yadlowsky",
"Negar Rostamzadeh",
"Katherine Heller"
] | [
"cs.LG",
"stat.ME",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-01-16T00:00:00 | https://arxiv.org/abs/2101.06536 | https://arxiv.org/pdf/2101.06536v6 | 2101.06536 | null | 84 | 12 | false | null | Machine Learning in Health Care | 0.557 |
97dc5527dac47eb7fd291ed110758cc2988b7ed557bf6779db571bbf4d48b22e | [
"arxiv",
"semantic_scholar"
] | A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning Tools | This paper discusses modern Auto Machine Learning (AutoML) tools from the perspective of a person with little prior experience in Machine Learning (ML). There are many AutoML tools both ready-to-use and under development, which are created to simplify and democratize usage of ML technologies in everyday life. Our posit... | [
"Oleg Bezrukavnikov",
"Rhema Linder"
] | [
"cs.LG",
"cs.HC"
] | [
"Computer Science"
] | 2021-01-14T00:00:00 | https://arxiv.org/abs/2101.05840 | https://arxiv.org/pdf/2101.05840v1 | 2101.05840 | null | 13 | 0 | false | null | arXiv.org | 0.2865 |
369da0984e61913874ffc54237339667e21f50f435fd3e3f769b0748a2b02970 | [
"arxiv",
"semantic_scholar"
] | Neural Architecture Search via Combinatorial Multi-Armed Bandit | Neural Architecture Search (NAS) has gained significant popularity as an effective tool for designing high performance deep neural networks (DNNs). NAS can be performed via policy gradient, evolutionary algorithms, differentiable architecture search or tree-search methods. While significant progress has been made for b... | [
"Hanxun Huang",
"Xingjun Ma",
"Sarah M. Erfani",
"James Bailey"
] | [
"cs.LG",
"cs.CV",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-01-01T00:00:00 | https://arxiv.org/abs/2101.00336 | https://arxiv.org/pdf/2101.00336v2 | 2101.00336 | 10.1109/IJCNN52387.2021.9533655 | 7 | 0 | false | null | IEEE International Joint Conference on Neural Network | 0.2258 |
6baaa9c619d817332aacd2479399e4e9ec43f73fb8704bb1a3fc65e8c7e2edb2 | [
"arxiv",
"semantic_scholar"
] | dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python | The increasing amount of available data, computing power, and the constant pursuit for higher performance results in the growing complexity of predictive models. Their black-box nature leads to opaqueness debt phenomenon inflicting increased risks of discrimination, lack of reproducibility, and deflated performance due... | [
"Hubert Baniecki",
"Wojciech Kretowicz",
"Piotr Piatyszek",
"Jakub Wisniewski",
"Przemyslaw Biecek"
] | [
"cs.LG",
"cs.HC",
"cs.SE",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-12-28T00:00:00 | https://arxiv.org/abs/2012.14406 | https://arxiv.org/pdf/2012.14406v2 | 2012.14406 | null | 132 | 10 | false | null | Journal of machine learning research | 0.531 |
04ccbe16eac3848d1f44493693ef8444c2ab6521d1a7451e01efdc827d7f31f3 | [
"arxiv",
"semantic_scholar"
] | Joint Search of Data Augmentation Policies and Network Architectures | The common pipeline of training deep neural networks consists of several building blocks such as data augmentation and network architecture selection. AutoML is a research field that aims at automatically designing those parts, but most methods explore each part independently because it is more challenging to simultane... | [
"Taiga Kashima",
"Yoshihiro Yamada",
"Shunta Saito"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2020-12-17T00:00:00 | https://arxiv.org/abs/2012.09407 | https://arxiv.org/pdf/2012.09407v2 | 2012.09407 | null | 5 | 1 | false | null | arXiv.org | 0.1945 |
ca59aae67cd7c34ed738725812439aabb1c032b0c33f16e2c177b3f80329158c | [
"arxiv",
"semantic_scholar"
] | Differential Evolution for Neural Architecture Search | Neural architecture search (NAS) methods rely on a search strategy for deciding which architectures to evaluate next and a performance estimation strategy for assessing their performance (e.g., using full evaluations, multi-fidelity evaluations, or the one-shot model). In this paper, we focus on the search strategy. We... | [
"Noor Awad",
"Neeratyoy Mallik",
"Frank Hutter"
] | [
"cs.NE",
"cs.LG"
] | [
"Computer Science"
] | 2020-12-11T00:00:00 | https://arxiv.org/abs/2012.06400 | https://arxiv.org/pdf/2012.06400v2 | 2012.06400 | null | 32 | 2 | false | null | arXiv.org | 0.3796 |
4b5bafe0552860d1506e0390e0a85f919da9478550bbf422f5be40f85b085358 | [
"arxiv",
"semantic_scholar"
] | Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance | Recently, Automated Machine Learning (AutoML) has registered increasing success with respect to tabular data. However, the question arises whether AutoML can also be applied effectively to text classification tasks. This work compares four AutoML tools on 13 different popular datasets, including Kaggle competitions, an... | [
"Matthias Blohm",
"Marc Hanussek",
"Maximilien Kintz"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2020-12-07T00:00:00 | https://arxiv.org/abs/2012.03575 | https://arxiv.org/pdf/2012.03575v1 | 2012.03575 | 10.5220/0010331411311136 | 23 | 4 | false | null | International Conference on Agents and Artificial Intelligence | 0.3495 |
4e41b626b57acfea47773d49f2390e19ac5545100911e8f4c73efc9792a9e03b | [
"arxiv",
"semantic_scholar"
] | A predictive model for kidney transplant graft survival using machine learning | Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-bas... | [
"Eric S. Pahl",
"W. Nick Street",
"Hans J. Johnson",
"Alan I. Reed"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics",
"Medicine"
] | 2020-12-07T00:00:00 | https://arxiv.org/abs/2012.03787 | https://arxiv.org/pdf/2012.03787v1 | 2012.03787 | 10.5121/csit.2020.101609 | 3 | 1 | false | null | 4th International Conference on Computer Science and Information Technology (COMIT 2020), November 28-29, 2020, Dubai, UAE. ISBN: 978-1-925953-30-5. Volume 10, Number 16 | 0.1505 |
476b26f890ebb15c122f3cbb25f1228cd904bd90d0569e098b9617e9a65b2090 | [
"arxiv",
"semantic_scholar"
] | FP-NAS: Fast Probabilistic Neural Architecture Search | Differential Neural Architecture Search (NAS) requires all layer choices to be held in memory simultaneously; this limits the size of both search space and final architecture. In contrast, Probabilistic NAS, such as PARSEC, learns a distribution over high-performing architectures, and uses only as much memory as needed... | [
"Zhicheng Yan",
"Xiaoliang Dai",
"Peizhao Zhang",
"Yuandong Tian",
"Bichen Wu",
"Matt Feiszli"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2020-11-22T00:00:00 | https://arxiv.org/abs/2011.10949 | https://arxiv.org/pdf/2011.10949v3 | 2011.10949 | 10.1109/CVPR46437.2021.01489 | 28 | 2 | false | null | Computer Vision and Pattern Recognition | 0.3656 |
eb171fe6d868ca7c4e290e7cf730802cde5e7bd03d59b4369ac88082c1b88ef1 | [
"arxiv",
"semantic_scholar"
] | PV-NAS: Practical Neural Architecture Search for Video Recognition | Recently, deep learning has been utilized to solve video recognition problem due to its prominent representation ability. Deep neural networks for video tasks is highly customized and the design of such networks requires domain experts and costly trial and error tests. Recent advance in network architecture search has ... | [
"Zihao Wang",
"Chen Lin",
"Lu Sheng",
"Junjie Yan",
"Jing Shao"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2020-11-02T00:00:00 | https://arxiv.org/abs/2011.00826 | https://arxiv.org/pdf/2011.00826v2 | 2011.00826 | null | 9 | 1 | false | null | arXiv.org | 0.25 |
e0798c085876bb8f3a67a394568ba1f1f84dfc792e03e96451fc13ba26d4f3ca | [
"arxiv",
"semantic_scholar"
] | AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data | Developing high-performing predictive models for large tabular data sets is a challenging task. The state-of-the-art methods are based on expert-developed model ensembles from different supervised learning methods. Recently, automated machine learning (AutoML) is emerging as a promising approach to automate predictive ... | [
"Romain Egele",
"Prasanna Balaprakash",
"Venkatram Vishwanath",
"Isabelle Guyon",
"Zhengying Liu"
] | [
"cs.LG",
"cs.NE",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-10-30T00:00:00 | https://arxiv.org/abs/2010.16358 | https://arxiv.org/pdf/2010.16358v2 | 2010.16358 | 10.1145/3458817.3476203 | 24 | 4 | false | null | International Conference for High Performance Computing, Networking, Storage and Analysis | 0.3495 |
e9f41e1c91dd9e1087e0a806f2be4a0c0ff28a497bda7055fca83afa3c399ff3 | [
"arxiv",
"semantic_scholar"
] | $μ$NAS: Constrained Neural Architecture Search for Microcontrollers | IoT devices are powered by microcontroller units (MCUs) which are extremely resource-scarce: a typical MCU may have an underpowered processor and around 64 KB of memory and persistent storage, which is orders of magnitude fewer computational resources than is typically required for deep learning. Designing neural netwo... | [
"Edgar Liberis",
"Łukasz Dudziak",
"Nicholas D. Lane"
] | [
"cs.LG",
"cs.AR"
] | [
"Computer Science"
] | 2020-10-27T00:00:00 | https://arxiv.org/abs/2010.14246 | https://arxiv.org/pdf/2010.14246v3 | 2010.14246 | 10.1145/3437984.3458836 | 133 | 16 | true | https://github.com/eliberis/uNAS | null | 0.6152 |
2a5c251e78e7217031ea110be822a4aec13b80b1cb0c0860b810a0f0dd678d3f | [
"arxiv",
"semantic_scholar"
] | Neural Architecture Search of SPD Manifold Networks | In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks, aiming to automate the design of SPD neural architectures. To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient S... | [
"Rhea Sanjay Sukthanker",
"Zhiwu Huang",
"Suryansh Kumar",
"Erik Goron Endsjo",
"Yan Wu",
"Luc Van Gool"
] | [
"cs.LG",
"cs.CV",
"cs.NE"
] | [
"Computer Science"
] | 2020-10-27T00:00:00 | https://arxiv.org/abs/2010.14535 | https://arxiv.org/pdf/2010.14535v4 | 2010.14535 | 10.24963/ijcai.2021/413 | 15 | 1 | false | null | International Joint Conference on Artificial Intelligence | 0.301 |
bc12acf1c01ad3348c4cba3cf86a1ad1b80c3ee530f2a2198a0cc07f0d42aa19 | [
"arxiv",
"semantic_scholar"
] | How Does Supernet Help in Neural Architecture Search? | Weight sharing, as an approach to speed up architecture performance estimation has received wide attention. Instead of training each architecture separately, weight sharing builds a supernet that assembles all the architectures as its submodels. However, there has been debate over whether the NAS process actually benef... | [
"Yuge Zhang",
"Quanlu Zhang",
"Yaming Yang"
] | [
"cs.LG",
"cs.CV",
"cs.NE"
] | [
"Computer Science"
] | 2020-10-16T00:00:00 | https://arxiv.org/abs/2010.08219 | https://arxiv.org/pdf/2010.08219v2 | 2010.08219 | null | 10 | 1 | false | null | arXiv.org | 0.2603 |
75d8f57c58ff18e04d863bea8d4f127031d6eb22863420d13e09aab0397e814e | [
"arxiv",
"semantic_scholar"
] | Direct Federated Neural Architecture Search | Neural Architecture Search (NAS) is a collection of methods to craft the way neural networks are built. We apply this idea to Federated Learning (FL), wherein predefined neural network models are trained on the client/device data. This approach is not optimal as the model developers can't observe the local data, and he... | [
"Anubhav Garg",
"Amit Kumar Saha",
"Debo Dutta"
] | [
"cs.LG",
"cs.NE"
] | [
"Computer Science"
] | 2020-10-13T00:00:00 | https://arxiv.org/abs/2010.06223 | https://arxiv.org/pdf/2010.06223v3 | 2010.06223 | null | 21 | 0 | false | null | arXiv.org | 0.3356 |
487665b09c46756b5856ba561c78cbe55c0daafe1304d901c170be19de8beacf | [
"arxiv",
"semantic_scholar"
] | ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding | Neural architecture search (NAS) aims to produce the optimal sparse solution from a high-dimensional space spanned by all candidate connections. Current gradient-based NAS methods commonly ignore the constraint of sparsity in the search phase, but project the optimized solution onto a sparse one by post-processing. As ... | [
"Yibo Yang",
"Hongyang Li",
"Shan You",
"Fei Wang",
"Chen Qian",
"Zhouchen Lin"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2020-10-13T00:00:00 | https://arxiv.org/abs/2010.06176 | https://arxiv.org/pdf/2010.06176v1 | 2010.06176 | null | 65 | 9 | false | null | Neural Information Processing Systems | 0.5 |
bc01508fceee7ec13b7b1d8097f2185612e1e1094c995103b23f7b302a3f4c21 | [
"arxiv",
"semantic_scholar"
] | Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning | In this work, a modified neural architecture search method (NAS) based physics-informed deep learning model is presented for stochastic analysis in heterogeneous porous material. Monte Carlo method based on a randomized spectral representation is first employed to construct a stochastic model for simulation of flow thr... | [
"Hongwei Guo",
"Xiaoying Zhuang",
"Timon Rabczuk"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2020-10-03T00:00:00 | https://arxiv.org/abs/2010.12344 | https://arxiv.org/pdf/2010.12344v2 | 2010.12344 | 10.1007/s00366-021-01586-2 | 92 | 1 | false | null | Engineering computations | 0.4921 |
cdb32c2bd659d511059508210f9f078b2a3f99780428bf1c7219dc52761d6ddc | [
"arxiv",
"semantic_scholar"
] | Theoretical Analysis of the Advantage of Deepening Neural Networks | We propose two new criteria to understand the advantage of deepening neural networks. It is important to know the expressivity of functions computable by deep neural networks in order to understand the advantage of deepening neural networks. Unless deep neural networks have enough expressivity, they cannot have good pe... | [
"Yasushi Esaki",
"Yuta Nakahara",
"Toshiyasu Matsushima"
] | [
"cs.LG",
"cs.NE",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-09-24T00:00:00 | https://arxiv.org/abs/2009.11479 | https://arxiv.org/pdf/2009.11479v1 | 2009.11479 | 10.1109/ICMLA51294.2020.00081 | 1 | 0 | false | null | International Conference on Machine Learning and Applications | 0.0753 |
0622085a08d05b03163bed1873252235b10c333e3ab92849a29e99242d3b2ff6 | [
"arxiv",
"semantic_scholar"
] | Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models | Software flaw detection using multimodal deep learning models has been demonstrated as a very competitive approach on benchmark problems. In this work, we demonstrate that even better performance can be achieved using neural architecture search (NAS) combined with multimodal learning models. We adapt a NAS framework ai... | [
"Alexis Cooper",
"Xin Zhou",
"Scott Heidbrink",
"Daniel M. Dunlavy"
] | [
"stat.ML",
"cs.AI",
"cs.CR",
"cs.LG"
] | [
"Mathematics",
"Computer Science"
] | 2020-09-22T00:00:00 | https://arxiv.org/abs/2009.10644 | https://arxiv.org/pdf/2009.10644v1 | 2009.10644 | 10.2172/1668457 | 4 | 0 | false | null | arXiv.org | 0.1747 |
34b152b8fe8ad33179ab4acbb63df58bf6a093b708a99d4ad0432755f4111178 | [
"arxiv",
"semantic_scholar"
] | Evolutionary Architecture Search for Graph Neural Networks | Automated machine learning (AutoML) has seen a resurgence in interest with the boom of deep learning over the past decade. In particular, Neural Architecture Search (NAS) has seen significant attention throughout the AutoML research community, and has pushed forward the state-of-the-art in a number of neural models to ... | [
"Min Shi",
"David A. Wilson",
"Xingquan Zhu",
"Yu Huang",
"Yuan Zhuang",
"Jianxun Liu",
"Yufei Tang"
] | [
"cs.NE"
] | [
"Computer Science"
] | 2020-09-21T00:00:00 | https://arxiv.org/abs/2009.10199 | https://arxiv.org/pdf/2009.10199v1 | 2009.10199 | null | 25 | 3 | false | null | arXiv.org | 0.3537 |
5e738188133c4e748dd13778b14cc43eeaada83831ecf64335eff81f780d1d69 | [
"arxiv",
"semantic_scholar"
] | Path Planning using Neural A* Search | We present Neural A*, a novel data-driven search method for path planning problems. Despite the recent increasing attention to data-driven path planning, machine learning approaches to search-based planning are still challenging due to the discrete nature of search algorithms. In this work, we reformulate a canonical A... | [
"Ryo Yonetani",
"Tatsunori Taniai",
"Mohammadamin Barekatain",
"Mai Nishimura",
"Asako Kanezaki"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-09-16T00:00:00 | https://arxiv.org/abs/2009.07476 | https://arxiv.org/pdf/2009.07476v3 | 2009.07476 | null | 144 | 18 | false | null | International Conference on Machine Learning | 0.6394 |
5871e1969be4bfa76ebcef7340a09da18c561dac4702d7ddd37c59760c3a12ad | [
"arxiv",
"semantic_scholar"
] | Architectural Implications of Graph Neural Networks | Graph neural networks (GNN) represent an emerging line of deep learning models that operate on graph structures. It is becoming more and more popular due to its high accuracy achieved in many graph-related tasks. However, GNN is not as well understood in the system and architecture community as its counterparts such as... | [
"Zhihui Zhang",
"Jingwen Leng",
"Lingxiao Ma",
"Youshan Miao",
"Chao Li",
"Minyi Guo"
] | [
"cs.AR",
"cs.LG",
"cs.PF"
] | [
"Computer Science"
] | 2020-09-02T00:00:00 | https://arxiv.org/abs/2009.00804 | https://arxiv.org/pdf/2009.00804v2 | 2009.00804 | 10.1109/LCA.2020.2988991 | 44 | 4 | false | null | IEEE computer architecture letters | 0.4133 |
726d3a07a09acaf6b6ed93cf3d3a3b7b9efc13b7435d85e6a05bb45b81c5d384 | [
"arxiv",
"semantic_scholar"
] | NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size | Neural architecture search (NAS) has attracted a lot of attention and has been illustrated to bring tangible benefits in a large number of applications in the past few years. Architecture topology and architecture size have been regarded as two of the most important aspects for the performance of deep learning models a... | [
"Xuanyi Dong",
"Lu Liu",
"Katarzyna Musial",
"Bogdan Gabrys"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics",
"Medicine"
] | 2020-08-28T00:00:00 | https://arxiv.org/abs/2009.00437 | https://arxiv.org/pdf/2009.00437v6 | 2009.00437 | 10.1109/TPAMI.2021.3054824 | 210 | 44 | false | null | IEEE Transactions on Pattern Analysis and Machine Intelligence | 0.8266 |
a1eef03bcd2ce4da93332ffa5d20edff1003b6e392f8afcb0775097ffdb2be12 | [
"arxiv",
"semantic_scholar"
] | NASirt: AutoML based learning with instance-level complexity information | Designing adequate and precise neural architectures is a challenging task, often done by highly specialized personnel. AutoML is a machine learning field that aims to generate good performing models in an automated way. Spectral data such as those obtained from biological analysis have generally a lot of important info... | [
"Habib Asseiss Neto",
"Ronnie C. O. Alves",
"Sergio V. A. Campos"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-08-26T00:00:00 | https://arxiv.org/abs/2008.11846 | https://arxiv.org/pdf/2008.11846v2 | 2008.11846 | null | 4 | 0 | false | null | arXiv.org | 0.1747 |
f089794bdd11c688ce156eee8937c657c140bad16615af55871103cc6d9aba89 | [
"arxiv",
"semantic_scholar"
] | NAS-DIP: Learning Deep Image Prior with Neural Architecture Search | Recent work has shown that the structure of deep convolutional neural networks can be used as a structured image prior for solving various inverse image restoration tasks. Instead of using hand-designed architectures, we propose to search for neural architectures that capture stronger image priors. Building upon a gene... | [
"Yun-Chun Chen",
"Chen Gao",
"Esther Robb",
"Jia-Bin Huang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2020-08-26T00:00:00 | https://arxiv.org/abs/2008.11713 | https://arxiv.org/pdf/2008.11713v1 | 2008.11713 | 10.1007/978-3-030-58523-5_26 | 62 | 9 | true | https://github.com/YunChunChen/NAS-DIP-pytorch | European Conference on Computer Vision | 0.5 |
217d67ae7bcc805d838fc6880dd1146f71c29ed0387018100daa117844c04c47 | [
"arxiv",
"semantic_scholar"
] | A Survey on Evolutionary Neural Architecture Search | Deep Neural Networks (DNNs) have achieved great success in many applications. The architectures of DNNs play a crucial role in their performance, which is usually manually designed with rich expertise. However, such a design process is labour intensive because of the trial-and-error process, and also not easy to realiz... | [
"Yuqiao Liu",
"Yanan Sun",
"Bing Xue",
"Mengjie Zhang",
"Gary G. Yen",
"Kay Chen Tan"
] | [
"cs.NE"
] | [
"Computer Science",
"Medicine"
] | 2020-08-25T00:00:00 | https://arxiv.org/abs/2008.10937 | https://arxiv.org/pdf/2008.10937v4 | 2008.10937 | 10.1109/TNNLS.2021.3100554 | 574 | 30 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.7457 |
46213f70f54769c684e1e2e26224f4f614ba2a7f0042a0585d3f790cbee1bd2d | [
"arxiv",
"semantic_scholar"
] | Automated Machine Learning -- a brief review at the end of the early years | Automated machine learning (AutoML) is the sub-field of machine learning that aims at automating, to some extend, all stages of the design of a machine learning system. In the context of supervised learning, AutoML is concerned with feature extraction, pre processing, model design and post processing. Major contributio... | [
"Hugo Jair Escalante"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-08-19T00:00:00 | https://arxiv.org/abs/2008.08516 | https://arxiv.org/pdf/2008.08516v3 | 2008.08516 | 10.1007/978-3-030-72069-8_2 | 35 | 2 | false | null | null | 0.3891 |
bf8ed4d00ad80e35b7bcb40b219b43a40ffc15e464633acf9a391d270c88da3e | [
"arxiv",
"semantic_scholar"
] | TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search | With the flourish of differentiable neural architecture search (NAS), automatically searching latency-constrained architectures gives a new perspective to reduce human labor and expertise. However, the searched architectures are usually suboptimal in accuracy and may have large jitters around the target latency. In thi... | [
"Yibo Hu",
"Xiang Wu",
"Ran He"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2020-08-12T00:00:00 | https://arxiv.org/abs/2008.05314 | https://arxiv.org/pdf/2008.05314v1 | 2008.05314 | 10.1007/978-3-030-58555-6_8 | 47 | 8 | true | https://github.com/AberHu/TF-NAS | European Conference on Computer Vision | 0.4771 |
6677d6d0c3251d42cfc189c079a07dd57f09a15d092ef9d36e521865e9804d11 | [
"arxiv",
"semantic_scholar"
] | HMCNAS: Neural Architecture Search using Hidden Markov Chains and Bayesian Optimization | Neural Architecture Search has achieved state-of-the-art performance in a variety of tasks, out-performing human-designed networks. However, many assumptions, that require human definition, related with the problems being solved or the models generated are still needed: final model architectures, number of layers to be... | [
"Vasco Lopes",
"Luís A. Alexandre"
] | [
"cs.LG",
"cs.CV",
"cs.NE",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-07-31T00:00:00 | https://arxiv.org/abs/2007.16149 | https://arxiv.org/pdf/2007.16149v1 | 2007.16149 | null | 1 | 0 | false | null | arXiv.org | 0.0753 |
a3b2ff184c04dbdfa70d1c00f31be92be2a3bb20288acb3312694c59ac00ebf5 | [
"arxiv",
"semantic_scholar"
] | Neural Architecture Search in Graph Neural Networks | Performing analytical tasks over graph data has become increasingly interesting due to the ubiquity and large availability of relational information. However, unlike images or sentences, there is no notion of sequence in networks. Nodes (and edges) follow no absolute order, and it is hard for traditional machine learni... | [
"Matheus Nunes",
"Gisele L. Pappa"
] | [
"cs.NE",
"cs.LG"
] | [
"Computer Science"
] | 2020-07-31T00:00:00 | https://arxiv.org/abs/2008.00077 | https://arxiv.org/pdf/2008.00077v1 | 2008.00077 | 10.1007/978-3-030-61377-8_21 | 36 | 1 | false | null | Brazilian Conference on Intelligent Systems | 0.3921 |
96f541162fe08f6bb40278de393b8064d98336778a0d76e8b6e03ccf60869aa7 | [
"arxiv",
"semantic_scholar"
] | On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice | Machine learning algorithms have been used widely in various applications and areas. To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model's performance. It often requires... | [
"Li Yang",
"Abdallah Shami"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-07-30T00:00:00 | https://arxiv.org/abs/2007.15745 | https://arxiv.org/pdf/2007.15745v3 | 2007.15745 | 10.1016/j.neucom.2020.07.061 | 2,827 | 84 | true | https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms | Neurocomputing | 0.9647 |
ff7965e5814e9639a868918e7a374c0cea96bb398b2f3cc9d480bb880e9066cc | [
"arxiv",
"semantic_scholar"
] | Tighter risk certificates for neural networks | This paper presents an empirical study regarding training probabilistic neural networks using training objectives derived from PAC-Bayes bounds. In the context of probabilistic neural networks, the output of training is a probability distribution over network weights. We present two training objectives, used here for t... | [
"María Pérez-Ortiz",
"Omar Rivasplata",
"John Shawe-Taylor",
"Csaba Szepesvári"
] | [
"cs.LG",
"cs.CV",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-07-25T00:00:00 | https://arxiv.org/abs/2007.12911 | https://arxiv.org/pdf/2007.12911v3 | 2007.12911 | null | 130 | 26 | false | null | Journal of machine learning research | 0.7157 |
95cc4f798f5104835395270b84964de49c282a495adcac895562fa97612d219d | [
"arxiv",
"semantic_scholar"
] | Graph topology inference benchmarks for machine learning | Graphs are nowadays ubiquitous in the fields of signal processing and machine learning. As a tool used to express relationships between objects, graphs can be deployed to various ends: I) clustering of vertices, II) semi-supervised classification of vertices, III) supervised classification of graph signals, and IV) den... | [
"Carlos Lassance",
"Vincent Gripon",
"Gonzalo Mateos"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-07-16T00:00:00 | https://arxiv.org/abs/2007.08216 | https://arxiv.org/pdf/2007.08216v1 | 2007.08216 | 10.1109/MLSP49062.2020.9231794 | 4 | 0 | true | https://github.com/cadurosar/benchmark_graphinference | International Workshop on Machine Learning for Signal Processing | 0.1747 |
4d1bb218252a70e4224d3e0eca1833958d92b0279485948d8fedf06fa87ab569 | [
"arxiv",
"semantic_scholar"
] | MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation | The recent breakthroughs of Neural Architecture Search (NAS) have motivated various applications in medical image segmentation. However, most existing work either simply rely on hyper-parameter tuning or stick to a fixed network backbone, thereby limiting the underlying search space to identify more efficient architect... | [
"Xingang Yan",
"Weiwen Jiang",
"Yiyu Shi",
"Cheng Zhuo"
] | [
"eess.IV",
"cs.CV"
] | [
"Computer Science",
"Engineering"
] | 2020-07-13T00:00:00 | https://arxiv.org/abs/2007.06151 | https://arxiv.org/pdf/2007.06151v1 | 2007.06151 | 10.1007/978-3-030-59710-8_38 | 52 | 5 | false | null | International Conference on Medical Image Computing and Computer-Assisted Intervention | 0.4311 |
2b12fbea59601eea15d2fba515aa9269d9c8d8e0098a38d352d0fecb1e55e4bf | [
"arxiv",
"semantic_scholar"
] | Impact of Legal Requirements on Explainability in Machine Learning | The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques. | [
"Adrien Bibal",
"Michael Lognoul",
"Alexandre de Streel",
"Benoît Frénay"
] | [
"cs.AI",
"cs.CY",
"cs.LG"
] | [
"Computer Science"
] | 2020-07-10T00:00:00 | https://arxiv.org/abs/2007.05479 | https://arxiv.org/pdf/2007.05479v1 | 2007.05479 | null | 10 | 1 | false | null | International Conference on Machine Learning | 0.2603 |
47daae5058cb6d879037736eb3b9717efe34c15b04f6988c0f89e2a12f0082c7 | [
"arxiv",
"semantic_scholar"
] | GAMA: a General Automated Machine learning Assistant | The General Automated Machine learning Assistant (GAMA) is a modular AutoML system developed to empower users to track and control how AutoML algorithms search for optimal machine learning pipelines, and facilitate AutoML research itself. In contrast to current, often black-box systems, GAMA allows users to plug in dif... | [
"Pieter Gijsbers",
"Joaquin Vanschoren"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-07-09T00:00:00 | https://arxiv.org/abs/2007.04911 | https://arxiv.org/pdf/2007.04911v2 | 2007.04911 | 10.1007/978-3-030-67670-4_39 | 32 | 0 | false | null | Lecture Notes in Computer Science, vol 12461 (2021). p560-564 | 0.3796 |
ff5cfbc063e390ef9caf5392f5ae7c8d31951dc258e4bc3a8436fdbce26e043f | [
"arxiv",
"semantic_scholar"
] | A Study on Encodings for Neural Architecture Search | Neural architecture search (NAS) has been extensively studied in the past few years. A popular approach is to represent each neural architecture in the search space as a directed acyclic graph (DAG), and then search over all DAGs by encoding the adjacency matrix and list of operations as a set of hyperparameters. Recen... | [
"Colin White",
"Willie Neiswanger",
"Sam Nolen",
"Yash Savani"
] | [
"cs.LG",
"cs.NE",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-07-09T00:00:00 | https://arxiv.org/abs/2007.04965 | https://arxiv.org/pdf/2007.04965v2 | 2007.04965 | null | 85 | 12 | true | https://github.com/naszilla/nas-encodings | Neural Information Processing Systems | 0.557 |
ed39a5eb65769830e84ea685a067d69e173f3fad050a2467fbcc13fdb306e6b7 | [
"arxiv",
"semantic_scholar"
] | Accuracy Prediction with Non-neural Model for Neural Architecture Search | Neural architecture search (NAS) with an accuracy predictor that predicts the accuracy of candidate architectures has drawn increasing attention due to its simplicity and effectiveness. Previous works usually employ neural network-based predictors which require more delicate design and are easy to overfit. Considering ... | [
"Renqian Luo",
"Xu Tan",
"Rui Wang",
"Tao Qin",
"Enhong Chen",
"Tie-Yan Liu"
] | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-07-09T00:00:00 | https://arxiv.org/abs/2007.04785 | https://arxiv.org/pdf/2007.04785v3 | 2007.04785 | null | 14 | 1 | true | https://github.com/renqianluo/GBDT-NAS | null | 0.294 |
3bf168494071c4a03bced385d3cb780f8162268b97ba5fdcaaffd0a8865a506c | [
"arxiv",
"semantic_scholar"
] | Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning | Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial success. In this paper, we introduce new AutoML approaches motivated by our winning submission to the second ChaLearn AutoML challenge. We develo... | [
"Matthias Feurer",
"Katharina Eggensperger",
"Stefan Falkner",
"Marius Lindauer",
"Frank Hutter"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-07-08T00:00:00 | https://arxiv.org/abs/2007.04074 | https://arxiv.org/pdf/2007.04074v3 | 2007.04074 | null | 395 | 30 | false | null | Journal of machine learning research | 0.7457 |
5038df577d6ab19f1ccafcb42ac6b0f44b2c097c07c21f992e48a7a680d2d49c | [
"arxiv",
"semantic_scholar"
] | Lale: Consistent Automated Machine Learning | Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies. Unfortunately, the syntax for automated machine learning tools is inconsistent with manual machine learning, with each other, and with err... | [
"Guillaume Baudart",
"Martin Hirzel",
"Kiran Kate",
"Parikshit Ram",
"Avraham Shinnar"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2020-07-04T00:00:00 | https://arxiv.org/abs/2007.01977 | https://arxiv.org/pdf/2007.01977v1 | 2007.01977 | null | 25 | 1 | false | null | arXiv.org | 0.3537 |
249b9ca5af007148811adcb7fe10520498b81976d3a74b81c2b3d3aef4a1f90e | [
"arxiv",
"semantic_scholar"
] | Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location | Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing with patients who come from the emergency department. In this work we propose a student-teacher network via reinforcement learning to deal with... | [
"Rasheed el-Bouri",
"David Eyre",
"Peter Watkinson",
"Tingting Zhu",
"David Clifton"
] | [
"cs.LG",
"cs.CV",
"stat.ML"
] | [
"Computer Science",
"Psychology",
"Mathematics"
] | 2020-07-01T00:00:00 | https://arxiv.org/abs/2007.01135 | https://arxiv.org/pdf/2007.01135v1 | 2007.01135 | null | 37 | 0 | false | null | International Conference on Machine Learning | 0.3949 |
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