Déplacer des tas de terre dans votre jardin relève d’un problème mathématique complexe dit du « transport optimal ». L’équation de la distance de Wasserstein montre comment optimiser vos efforts… et permet aussi d’améliorer les prévisions météorologiques et l’analyse d’images !
| # | Наименование новости | Тональность | Информативность | Дата публикации |
|---|---|---|---|---|
| 1 | Global Fr{\'{e}}chet Manifold Learning for Random Objects, With Application to Low-Dimensional Wasserstein Representations of Distributional Data | 0 | 5.55 | 17-08-2026 |
| 2 | skwdro: a library for Wasserstein distributionally robust machine learning | 0 | 4.74 | 17-08-2026 |
| 3 | A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization | 0 | 9.82 | 17-08-2026 |
| 4 | Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection | 0 | 4.23 | 17-08-2026 |
| 5 | Node Regression on Latent Position Random Graphs via Local Averaging | 0 | 4.07 | 17-08-2026 |
| 6 | Demographic Parity in Regression and Classification Within the Unawareness Framework | 0 | 4.33 | 17-08-2026 |
| 7 | End-to-End Deep Learning for Predicting Metric Space-Valued Outputs | 0 | 10.66 | 17-08-2026 |
| 8 | Nonparametric generative modeling for time series via Schr{\"{o}}dinger bridge | 0 | 5.53 | 17-08-2026 |
| 9 | The math of traffic jams and how to avoid them | 0 | 9.27 | 25-07-2026 |