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Scaling down Deep Learning
Though deep learning models have taken on commercial and political relev...
11/29/2020 ∙ by Sam Greydanus, et al. ∙
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Lagrangian Neural Networks
Accurate models of the world are built upon notions of its underlying sy...
03/10/2020 ∙ by Miles Cranmer, et al. ∙
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Neural reparameterization improves structural optimization
Structural optimization is a popular method for designing objects such a...
09/10/2019 ∙ by Stephan Hoyer, et al. ∙
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Hamiltonian Neural Networks
Even though neural networks enjoy widespread use, they still struggle to...
06/04/2019 ∙ by Sam Greydanus, et al. ∙
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Learning Finite State Representations of Recurrent Policy Networks
Recurrent neural networks (RNNs) are an effective representation of cont...
11/29/2018 ∙ by Anurag Koul, et al. ∙
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Visualizing and Understanding Atari Agents
Deep reinforcement learning (deep RL) agents have achieved remarkable su...
10/31/2017 ∙ by Sam Greydanus, et al. ∙
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Learning the Enigma with Recurrent Neural Networks
Recurrent neural networks (RNNs) represent the state of the art in trans...
08/24/2017 ∙ by Sam Greydanus, et al. ∙
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