DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks

1 code implementation • NeurIPS 2021 • Wei Sun, Aojun Zhou, Sander Stuijk, Rob Wijnhoven, Andrew Oakleigh Nelson, Hongsheng Li, Henk Corporaal

However, the existing N:M algorithms only address the challenge of how to train N:M sparse neural networks in a uniform fashion (i. e. every layer has the same N:M sparsity) and suffer from a significant accuracy drop for high sparsity (i. e. when sparsity > 80\%).

Network Pruning

Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction

no code implementations • 23 Jun 2020 • Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider

We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models in various gray-box settings which incorporates prior knowledge in the form of systems of ordinary differential equations.

Neural Dynamical Systems

no code implementations • ICLR Workshop DeepDiffEq 2019 • Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider

We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models which incorporates prior knowledge in the form of systems of ordinary differential equations.

Offline Contextual Bayesian Optimization for Nuclear Fusion

no code implementations • 6 Jan 2020 • Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider

One obstacle in utilizing fusion as a feasible energy source is the stability of the reaction.

Bayesian Optimization