The projected kernel is trained at multiple scales derived from using repeated computation of multiwavelet transform. This allows learning the complex dependencies at various scales and results in a resolution-independent scheme.

Imbalanced data pose challenges for deep learning based classification models. One of the most widely-used approaches for tackling imbalanced data is re-weighting, where training samples are associated with different weights in the loss function. Most of existing re-weighting approaches treat the example weights as the learnable parameter and optimize the weights on the meta set, entailing expensive bilevel optimization. In this paper, we propose a novel re-weighting method based on optimal transport (OT) from a distributional point of view. Specifically, we view the training set as an imbalanced distribution over its samples, which is transported by OT to a balanced distribution obtained from the meta set. The weights of the training samples are the probability mass of the imbalanced distribution and learned by minimizing the OT distance between the two distributions.

However, their effective capacity is often insufficient since the bijectivity constraint limits the model width.

We also introduce the Axial-Shift operation for different window interactions.

Training foundation models, such as GPT -3 and PaLM, can be extremely expensive, often involving tens of thousands of GPUs running continuously for months.

Tabular datasets are the last "unconquered castle" for deep learning, with traditional

Hence, the running time of each iteration does not take the sparsity of a given graph into account.