• Publications
  • Influence
Neural Message Passing for Quantum Chemistry
TLDR
Using MPNNs, state of the art results on an important molecular property prediction benchmark are demonstrated and it is believed future work should focus on datasets with larger molecules or more accurate ground truth labels.
Relational inductive biases, deep learning, and graph networks
TLDR
It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.
Sanity Checks for Saliency Maps
TLDR
It is shown that some existing saliency methods are independent both of the model and of the data generating process, and methods that fail the proposed tests are inadequate for tasks that are sensitive to either data or model.
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
TLDR
AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half.
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
TLDR
Concept Activation Vectors (CAVs) are introduced, which provide an interpretation of a neural net's internal state in terms of human-friendly concepts, and may be used to explore hypotheses and generate insights for a standard image classification network as well as a medical application.
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
TLDR
It is found that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work.
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
We propose a new technique, Singular Vector Canonical Correlation Analysis (SVCCA), a tool for quickly comparing two representations in a way that is both invariant to affine transform (allowing
Deep Information Propagation
TLDR
The presence of dropout destroys the order-to-chaos critical point and therefore strongly limits the maximum trainable depth for random networks, and a mean field theory for backpropagation is developed that shows that the ordered and chaotic phases correspond to regions of vanishing and exploding gradient respectively.
Adversarial Patch
TLDR
A method to create universal, robust, targeted adversarial image patches in the real world, which can be printed, added to any scene, photographed, and presented to image classifiers; even when the patches are small, they cause the classifiers to ignore the other items in the scene and report a chosen target class.
A Fourier Perspective on Model Robustness in Computer Vision
TLDR
AutoAugment, a recently proposed data augmentation policy optimized for clean accuracy, achieves state-of-the-art robustness on the CIFAR-10-C benchmark and is observed to use a more diverse set of augmentations than previously observed.
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