Distributed Learning via Filtered Hyperinterpolation on Manifolds | 0 | 0.34 | 2022 |
Learning Curves for Gaussian Process Regression with Power-Law Priors and Targets | 0 | 0.34 | 2022 |
Wasserstein distance to independence models | 0 | 0.34 | 2021 |
On the Expected Complexity of Maxout Networks. | 0 | 0.34 | 2021 |
Wasserstein Proximal of GANs | 0 | 0.34 | 2021 |
Information Complexity And Generalization Bounds | 0 | 0.34 | 2021 |
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers | 0 | 0.34 | 2020 |
Factorized Mutual Information Maximization | 0 | 0.34 | 2020 |
Factorized Mutual Information Maximization. | 0 | 0.34 | 2019 |
Wasserstein of Wasserstein Loss for Learning Generative Models. | 0 | 0.34 | 2019 |
Optimal Transport to a Variety. | 0 | 0.34 | 2019 |
Affine Natural Proximal Learning | 0 | 0.34 | 2019 |
Geometry and Determinism of Optimal Stationary Control in Partially Observable Markov Decision Processes. | 1 | 0.38 | 2015 |
Universal Approximation of Markov Kernels by Shallow Stochastic Feedforward Networks. | 0 | 0.34 | 2015 |
Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping | 5 | 0.47 | 2015 |
A Framework for Cheap Universal Approximation in Embodied Systems. | 1 | 0.38 | 2014 |