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README.md
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@@ -70,7 +70,7 @@ GARFIELD infers accurate motion from very sparse information and, with only four
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### Direct Density Decoding
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<p align="center">
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<img src="docs/
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</p>
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The density decoder achieves superior discrete energy scores while estimating motion densities orders of magnitude faster than Monte-Carlo sampling.
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### Entropy-informed Conditioning
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<p align="center">
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<img src="docs/
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</p>
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Selecting additional constraints by entropy collapses uncertainty without ground-truth errors and reaches comparable performance with fewer conditioning points.
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Conda (recommended):
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```bash
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conda create -n
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conda activate
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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```
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### Direct Density Decoding
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<p align="center">
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<img src="docs/energy_score_vs_latency_new.svg" alt="Discrete Energy Scores" width="500px">
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</p>
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The density decoder achieves superior discrete energy scores while estimating motion densities orders of magnitude faster than Monte-Carlo sampling.
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### Entropy-informed Conditioning
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<p align="center">
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<img src="docs/entropy_vs_epe_cond_new_data.svg" alt="Entropy-based cond" width="500px">
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</p>
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Selecting additional constraints by entropy collapses uncertainty without ground-truth errors and reaches comparable performance with fewer conditioning points.
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Conda (recommended):
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```bash
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conda create -n garfield python=3.12 -y
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conda activate garfield
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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```
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