| A Benchmark for Interpretability Methods in Deep Neural Networks | Sara Hooker · Dumitru Erhan · Pieter-Jan Kindermans · Been Kim |
| Accurate, reliable and fast robustness evaluation | Wieland Brendel · Jonas Rauber · Matthias Kümmerer · Ivan Ustyuzhaninov · Matthias Bethge |
| Approximate Feature Collisions in Neural Nets | Ke Li · Tianhao Zhang · Jitendra Malik |
| Computing Linear Restrictions of Neural Networks | Matthew Sotoudeh · Aditya V Thakur |
| CXPlain: Causal Explanations for Model Interpretation under Uncertainty | Patrick Schwab · Walter Karlen |
| Deliberative Explanations: visualizing network insecurities | Pei Wang · Nuno Nvasconcelos |
| Explanations can be manipulated and geometry is to blame | Ann-Kathrin Dombrowski · Maximillian Alber · Christopher Anders · Marcel Ackermann · Klaus-Robert Müller · Pan Kessel |
| Fooling Neural Network Interpretations via Adversarial Model Manipulation | Juyeon Heo · Sunghwan Joo · Taesup Moon |
| Full-Gradient Representation for Neural Network Visualization | Suraj Srinivas · François Fleuret |
| Grid Saliency for Context Explanations of Semantic Segmentation | Lukas Hoyer · Mauricio Munoz · Prateek Katiyar · Anna Khoreva · Volker Fischer |
| Intrinsic dimension of data representations in deep neural networks | Alessio Ansuini · Alessandro Laio · Jakob H Macke · Davide Zoccolan |
| One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers | Ari Morcos · Haonan Yu · Michela Paganini · Yuandong Tian |
| The Geometry of Deep Networks: Power Diagram Subdivision | Randall Balestriero · Romain Cosentino · Behnaam Aazhang · Richard Baraniuk |
| Visualizing and Measuring the Geometry of BERT | Emily Reif · Ann Yuan · Martin Wattenberg · Fernanda B Viegas · Andy Coenen · Adam Pearce · Been Kim |
| Visualizing the PHATE of Neural Networks | Scott Gigante · Adam S Charles · Smita Krishnaswamy · Gal Mishne |