3.3k citations · 3.4k across the 6 of their papers we have counts for
5 papers · 1 filter
Modularity Trumps Invariance for Compositional Robustness
Ian Mason, Anirban Sarkar, Tomotake Sasaki +1
By default neural networks are not robust to changes in data distribution. This has been demonstrated with simple image corruptions, such as blurring or adding noise, degrading ima…
Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts
Anirban Sarkar, Matthew Groth, Ian Mason +2
Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios. In this paper, we introduce a tool to visualize and understand such failures. We draw inspiration from conc…
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach
Anindya Sarkar, Anirban Sarkar, Sowrya Gali +1
Current SOTA adversarially robust models are mostly based on adversarial training (AT) and differ only by some regularizers either at inner maximization or outer minimization steps…
A Framework for Learning Ante-hoc Explainable Models via Concepts
Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar +1
Self-explaining deep models are designed to learn the latent concept-based explanations implicitly during training, which eliminates the requirement of any post-hoc explanation gen…
Neural Network Attributions: A Causal Perspective
Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar +1
We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architectur…