| Compressing gans using knowledge distillation A Aguinaldo, PY Chiang, A Gain, A Patil, K Pearson, S Feizi arXiv preprint arXiv:1902.00159, 2019 | 91 | 2019 |
| Understanding catastrophic forgetting and remembering in continual learning with optimal relevance mapping P Kaushik, A Gain, A Kortylewski, A Yuille arXiv preprint arXiv:2102.11343, 2021 | 54 | 2021 |
| Structure learning under missing data A Gain, I Shpitser International conference on probabilistic graphical models, 121-132, 2018 | 27 | 2018 |
| Abstraction mechanisms predict generalization in deep neural networks A Gain, H Siegelmann International Conference on Machine Learning, 3357-3366, 2020 | 6 | 2020 |
| Adaptive neural connections for sparsity learning A Gain, P Kaushik, H Siegelmann Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020 | 1 | 2020 |
| Relating information complexity and training in deep neural networks A Gain, H Siegelmann Micro-and Nanotechnology Sensors, Systems, and Applications XI 10982, 409-417, 2019 | 1 | 2019 |
| Utilizing full neuronal states for adversarial robustness A Gain, HT Siegelmann SPIE Future Sensing Technologies 11197, 146-148, 2019 | | 2019 |
| Deep Neural Networks Abstract Like Humans. A Gain, H Siegelmann CoRR, 2019 | | 2019 |
| Network of Spiking Neurons Driven by Compression A Gain, L Holder 2016 Data Compression Conference (DCC), 593-593, 2016 | | 2016 |
| Supplementary Material to Abstraction Mechanisms Predict Generalization in Deep Neural Networks A Gain, H Siegelmann | | |
