23 citations · 43 across the 4 of their papers we have counts for
5 papers · 1 filter
Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately
Andrew Sosanya, Sam Greydanus
Understanding natural symmetries is key to making sense of our complex and ever-changing world. Recent work has shown that neural networks can learn such symmetries directly from d…
Piecewise-constant Neural ODEs
Sam Greydanus, Stefan Lee, Alan Fern
Neural networks are a popular tool for modeling sequential data but they generally do not treat time as a continuous variable. Neural ODEs represent an important exception: they pa…
Lagrangian Neural Networks
Miles Cranmer, Sam Greydanus, Stephan Hoyer +3
Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet…
Neural reparameterization improves structural optimization
Stephan Hoyer, Jascha Sohl-Dickstein, Sam Greydanus
Structural optimization is a popular method for designing objects such as bridge trusses, airplane wings, and optical devices. Unfortunately, the quality of solutions depends heavi…
Learning Finite State Representations of Recurrent Policy Networks
Anurag Koul, Sam Greydanus, Alan Fern
Recurrent neural networks (RNNs) are an effective representation of control policies for a wide range of reinforcement and imitation learning problems. RNN policies, however, are p…