| Cnn-cert: An efficient framework for certifying robustness of convolutional neural networks A Boopathy, TW Weng, PY Chen, S Liu, L Daniel Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3240-3247, 2019 | 166 | 2019 |
| PROVEN: Verifying robustness of neural networks with a probabilistic approach L Weng, PY Chen, L Nguyen, M Squillante, A Boopathy, I Oseledets, ... International Conference on Machine Learning, 6727-6736, 2019 | 85 | 2019 |
| Proper network interpretability helps adversarial robustness in classification A Boopathy, S Liu, G Zhang, C Liu, PY Chen, S Chang, L Daniel International Conference on Machine Learning, 1014-1023, 2020 | 66 | 2020 |
| Double descent demystified: Identifying, interpreting & ablating the sources of a deep learning puzzle R Schaeffer, M Khona, Z Robertson, A Boopathy, K Pistunova, JW Rocks, ... arXiv preprint arXiv:2303.14151, 2023 | 11 | 2023 |
| How to train your wide neural network without backprop: An input-weight alignment perspective A Boopathy, I Fiete International Conference on Machine Learning, 2178-2205, 2022 | 6 | 2022 |
| Fast training of provably robust neural networks by singleprop A Boopathy, L Weng, S Liu, PY Chen, G Zhang, L Daniel Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 6803-6811, 2021 | 6 | 2021 |
| Model-agnostic measure of generalization difficulty A Boopathy, K Liu, J Hwang, S Ge, A Mohammedsaleh, IR Fiete International Conference on Machine Learning, 2857-2884, 2023 | 3 | 2023 |
| Divergence at the interpolation threshold: Identifying, interpreting & ablating the sources of a deep learning puzzle R Schaeffer, Z Robertson, A Boopathy, M Khona, I Fiete, A Gromov, ... NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning, 2023 | 2 | 2023 |
| Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity J Hwang, ZW Hong, E Chen, A Boopathy, P Agrawal, I Fiete arXiv preprint arXiv:2310.17537, 2023 | 2 | 2023 |
| Framework for certifying a lower bound on a robustness level of convolutional neural networks PY Chen, S Liu, A Boopathy, TW Weng, L Daniel US Patent 11,625,487, 2023 | 2 | 2023 |
| Interpretability-aware adversarial attack and defense method for deep learnings S Liu, G Zhang, PY Chen, C Gan, A Boopathy US Patent 11,397,891, 2022 | 2 | 2022 |
| Gradient-trained Weights in Wide Neural Networks Align Layerwise to Error-scaled Input Correlations A Boopathy, I Fiete arXiv preprint arXiv:2106.08453, 2021 | 1 | 2021 |
| Visual Interpretability Alone Helps Adversarial Robustness A Boopathy, S Liu, G Zhang, PY Chen, S Chang, L Daniel | 1 | 2019 |
| Towards Exact Computation of Inductive Bias A Boopathy, W Yue, J Hwang, A Iyer, IR Fiete | | 2023 |
| Breaking Neural Network Scaling Laws with Modularity A Boopathy, S Jiang, W Yue, J Hwang, A Iyer, IR Fiete | | 2023 |
| Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building J Hwang, ZW Hong, ER Chen, A Boopathy, P Agrawal, IR Fiete | | 2023 |
| Rapid Learning without Catastrophic Forgetting in the Morris Water Maze R Wang, J Hwang, A Boopathy, IR Fiete | | 2023 |
| Neuro-Inspired Efficient Map Building via Fragmentation and Recall J Hwang, ZW Hong, E Chen, A Boopathy, P Agrawal, I Fiete arXiv preprint arXiv:2307.05793, 2023 | | 2023 |
| Efficient Exploration via Fragmentation and Recall J Hwang, ZW Hong, ER Chen, A Boopathy, P Agrawal, IR Fiete | | 2022 |
| Towards More Generalizable Neural Networks via Modularity A Boopathy Massachusetts Institute of Technology, 2022 | | 2022 |
