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2210.12153
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On amortizing convex conjugates for optimal transport
21 October 2022
Brandon Amos
OT
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Papers citing
"On amortizing convex conjugates for optimal transport"
46 / 46 papers shown
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Supervised Training of Conditional Monge Maps
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Andreas Krause
Marco Cuturi
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131
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Meta Optimal Transport
Brandon Amos
Samuel N. Cohen
Giulia Luise
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Neural Optimal Transport with General Cost Functionals
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Evgeny Burnaev
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Amortized Proximal Optimization
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Paul Vicol
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Learning Predictions for Algorithms with Predictions
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Generative Modeling with Optimal Transport Maps
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Implicit Riemannian Concave Potential Maps
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Thomas Lavastida
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Riemannian Convex Potential Maps
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Brandon Amos
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Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
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Lingxiao Li
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Justin Solomon
Alexander N. Filippov
Evgeny Burnaev
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143
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Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
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Yair Schiff
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Large-Scale Wasserstein Gradient Flows
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Learning to Optimize: A Primer and A Benchmark
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Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
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Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
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Ricky T. Q. Chen
Christos Tsirigotis
Aaron Courville
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Continuous Regularized Wasserstein Barycenters
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Aude Genevay
Mikhail Yurochkin
Justin Solomon
65
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Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
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Amirhossein Taghvaei
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Multi-marginal Wasserstein GAN
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Optimal transport mapping via input convex neural networks
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Adversarial Computation of Optimal Transport Maps
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2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANs
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Glow: Generative Flow with Invertible 1x1 Convolutions
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Wasserstein Divergence for GANs
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Continuously Differentiable Exponential Linear Units
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Input Convex Neural Networks
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Stochastic Optimization for Large-scale Optimal Transport
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A Multi-Batch L-BFGS Method for Machine Learning
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Identity Mappings in Deep Residual Networks
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