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Found 7 papers, 5 papers with code
$k$-Mixup Regularization for Deep Learning via Optimal Transport
To better leverage the structure of the data, we extend mixup to $k$-mixup by perturbing $k$-batches of training points in the direction of other $k$-batches using displacement interpolation, i. e. interpolation under the Wasserstein metric.
k-Mixup Regularization for Deep Learning via Optimal Transport
Our empirical results show that training with $k$-mixup further improves generalization and robustness across several network architectures and benchmark datasets of differing modalities.
Incorporating Unlabeled Data into Distributionally Robust Learning
We examine the performance of this new formulation on 14 real datasets and find that it often yields effective classifiers with nontrivial performance guarantees in situations where conventional DRL produces neither.
Alleviating Label Switching with Optimal Transport
Label switching is a phenomenon arising in mixture model posterior inference that prevents one from meaningfully assessing posterior statistics using standard Monte Carlo procedures.
Hierarchical Optimal Transport for Document Representation
The ability to measure similarity between documents enables intelligent summarization and analysis of large corpora.
Dynamical Optimal Transport on Discrete Surfaces
We propose a technique for interpolating between probability distributions on discrete surfaces, based on the theory of optimal transport.
Analysis of PDEs Numerical Analysis Numerical Analysis Optimization and Control
Stochastic Wasserstein Barycenters
We present a stochastic algorithm to compute the barycenter of a set of probability distributions under the Wasserstein metric from optimal transport.
