Nash equilibria can be effectively addressed.

Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it difficult to understand what truly contributes to their success.

To address this issue, we propose to evict tensors within a sliding window to ensure all evictions are contiguous and are immediately used.

In this paper, we introduce a new class of structured matrices, which unifies and generalizes structured classes from previous works.

Randomized smoothing is a powerful framework for making models provably robust against small changes to their inputs - by guaranteeing robustness of the majority vote when randomly adding noise before classification.

In this paper, we propose a provably efficient natural policy gradient algorithm called Spectral Dynamic Embedding Policy Optimization ( SDEPO) for two-player zero-sum stochastic Markov games with continuous state space and finite action space. In the policy evaluation procedure of our algorithm, a novel kernel embedding method is employed to construct a finite-dimensional linear approximations to the state-action value function.

Here we omit the computation of HJ PDEs for concisity. The model is trained for 90, 000 iterations. The model is also trained for 90, 000 iterations. For the disentanglement methods, we largely enrich the original MNIST dataset by adding the transformed images of the whole sequence. The generalization ability ( i.e., validation accuracy) can be thus regarded as a reasonable surrogate for the disentanglement ability.

Our novel setup brings new understandings to both disentanglement and equivariance .