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Found 3 papers, 3 papers with code
A Neural Network Approach Applied to Multi-Agent Optimal Control
Our approach is grid-free and scales efficiently to dimensions where grids become impractical or infeasible.
Optimization and Control
OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport
On five high-dimensional density estimation and generative modeling tasks, OT-Flow performs competitively to state-of-the-art CNFs while on average requiring one-fourth of the number of weights with an 8x speedup in training time and 24x speedup in inference.
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows
Neural ODEs are ordinary differential equations (ODEs) with neural network components.
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