Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to enhance the agent's generalization ability on unseen tasks.

The code is available in https://github.com/cathyhxh/DIFFER.

Multivariate time series (MTS) forecasting has shown great importance in numerous industries.

Most existing approaches for dealing with noisy labels broadly fall into two categories: noise-modeling-based methods and memorization-effects-based methods.

To achieve this goal, we provide a generative diffusion-based framework called MomentDiff, which simulates a typical human retrieval process from random browsing to gradual localization. Specifically, we first diffuse the real span to random noise, and learn to denoise the random noise to the original span with the guidance of similarity between text and video.

Data augmentation has been proven effective for training high-accuracy convolu-tional neural network classifiers by preventing overfitting.

In the supplementary material, we first introduce the pseudo algorithm of DA W . Then we clarify the Then, we provide a more detailed explanation of Figures 1, 2, 4, and 5, which are slightly abbreviated due to the limited space of the main paper. In the naive pseudo-labeling method, all pseudo labels are enrolled into training, i.e., E 1 + E 2, which is guaranteed by theoretical functional analysis in the next section. Inequality 45 holds true at all times. In this section, we provide more qualitative results between ours and other competitors.