375 citations · 1.9k across the 50 of their papers we have counts for
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An Empirical Study of Example Forgetting during Deep Neural Network Learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes +3
Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understa…
Learning Beam Search Policies via Imitation Learning
Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon
Beam search is widely used for approximate decoding in structured prediction problems. Models often use a beam at test time but ignore its existence at train time, and therefore do…
A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians
Haichen Li, Christopher Collins, Matteus Tanha +2
Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an…
Dual Policy Iteration
Wen Sun, Geoffrey J. Gordon, Byron Boots +1
Recently, a novel class of Approximate Policy Iteration (API) algorithms have demonstrated impressive practical performance (e.g., ExIt from [2], AlphaGo-Zero from [27]). This new…
Recurrent Predictive State Policy Networks
Ahmed Hefny, Zita Marinho, Wen Sun +2
We introduce Recurrent Predictive State Policy (RPSP) networks, a recurrent architecture that brings insights from predictive state representations to reinforcement learning in par…