Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks Felix Dangel
Neural Information Processing Systems
Physics-informed neural networks (PINNs) are infamous for being hard to train.
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Learning-Based Low-Rank Approximations
Piotr Indyk, Ali Vakilian, Yang Yuan
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Neural Information Processing Systems http://nips.cc/
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RUDDER: Return Decomposition for Delayed Rewards
Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, Sepp Hochreiter
Neural Information Processing Systems
Neural Information Processing Systems http://nips.cc/
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RUDDER: Return Decomposition for Delayed Rewards
Neural Information Processing Systems
reinforcement learning; delayed reward; reward redistribution; return decomposition; bias-variance; credit assignment; LSTM
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