61 citations · 66 across the 3 of their papers we have counts for
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cs.LG2021★ 2 cited
Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping
Prakhar Kaushik, Alex Gain, Adam Kortylewski +1
Catastrophic forgetting in neural networks is a significant problem for continual learning. A majority of the current methods replay previous data during training, which violates t…
cs.LG2019
Abstraction Mechanisms Predict Generalization in Deep Neural Networks
Alex Gain, Hava Siegelmann
A longstanding problem for Deep Neural Networks (DNNs) is understanding their puzzling ability to generalize well. We approach this problem through the unconventional angle of \tex…
cs.LG2019★ 61 cited
Compressing GANs using Knowledge Distillation
Angeline Aguinaldo, Ping-Yeh Chiang, Alex Gain +3
Generative Adversarial Networks (GANs) have been used in several machine learning tasks such as domain transfer, super resolution, and synthetic data generation. State-of-the-art G…