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Found 6 papers, 2 papers with code
Mixture of Experts Soften the Curse of Dimensionality in Operator Learning
In this paper, we construct a mixture of neural operators (MoNOs) between function spaces whose complexity is distributed over a network of expert neural operators (NOs), with each NO satisfying parameter scaling restrictions.
Breaking the Curse of Dimensionality with Distributed Neural Computation
This improves the optimal bounds for traditional non-distributed deep learning models, namely ReLU MLPs, which need $\mathcal{O}(\varepsilon^{-n/2})$ parameters to achieve the same accuracy.
Convergences for Minimax Optimization Problems over Infinite-Dimensional Spaces Towards Stability in Adversarial Training
This instability problem comes from the difficulty of the minimax optimization, and there have been various approaches in GANs and UDAs to overcome this problem.
Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation
We conclude by proposing a hypernetwork version of the subfamily of NOs as a surrogate model for the mentioned forward operator.
Theoretical Error Analysis of Entropy Approximation for Gaussian Mixture
Notably, Gal and Ghahramani [2016] proposed the approximate entropy that is the sum of the entropies of unimodal Gaussian distributions.
Spectral Pruning for Recurrent Neural Networks
Recurrent neural networks (RNNs) are a class of neural networks used in sequential tasks.
