motion
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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/entropy_vs_epe_cond_new_data.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.
@@ -79,7 +79,7 @@ The density decoder achieves superior discrete energy scores while estimating mo
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  ### Entropy-informed Conditioning
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  <p align="center">
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- <img src="docs/energy_score_vs_latency_new.svg" alt="Entropy-based conditioning" 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.
@@ -106,8 +106,8 @@ Finally, create an environment to run the repository:
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  Conda (recommended):
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  ```bash
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- conda create -n weather-rf python=3.12 -y
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- conda activate weather-rf
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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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  ```