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Computing Receptive Fields of Convolutional Neural Networks
Detailed derivations and open-source code to analyze the receptive fields of convnets.
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30 articles
🔬 Research
Distill.pub
The Paths Perspective on Value Learning
A closer look at how Temporal Difference Learning merges paths of experience for greater statistical efficiency
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A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features'
Six comments from the community and responses from the original authors
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A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Adversarial Example Researchers Need to Expand What is Meant by 'Robustness'
The main hypothesis in Ilyas et al. (2019) happens to be a special case of a more general principle that is commonly accepted in the robustness to distributional shift literature
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A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Robust Feature Leakage
An example project using webpack and svelte-loader and ejs to inline SVGs
🔬 Research
Distill.pub
A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Two Examples of Useful, Non-Robust Features
An example project using webpack and svelte-loader and ejs to inline SVGs