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06/15/2023
Modularity Trumps Invariance for Compositional Robustness
By default neural networks are not robust to changes in data distributio...
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03/17/2023
Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts
Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios....
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10/30/2021
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach
Current SOTA adversarially robust models are mostly based on adversarial...
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08/25/2021
Inducing Semantic Grouping of Latent Concepts for Explanations: An Ante-Hoc Approach
Self-explainable deep models are devised to represent the hidden concept...
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12/28/2020
Enhanced Regularizers for Attributional Robustness
Deep neural networks are the default choice of learning models for compu...
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02/06/2019
Neural Network Attributions: A Causal Perspective
We propose a new attribution method for neural networks developed using ...
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10/30/2017
