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School of Mathematical Sciences, Shanghai Jiao Tong University
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Zero-loss exactness in optimal transport dynamics could revolutionize how we approach matching problems in high-dimensional spaces.
Forget gradients: this new sampler learns complex distributions, even with discrete parameters, by enforcing time-reversibility and comparing forward and backward Markov trajectories.
