These authors contributed equally and are ordered alphabetically.

A vast majority of prior work on robustness however assumes data homogeneity, i.e., local datasets are generated from the same distribution.

We present a novel self-supervised approach for hierarchical representation learning and segmentation of perceptual inputs in a streaming fashion.

The local search methods have been widely used to solve the clustering problems. In practice, local search algorithms for clustering problems mainly adapt the single-swap strategy, which enables them to handle large-scale datasets and achieve linear running time in the data size.

Several recent works demonstrate that transformers can implement algorithms like gradient descent. By a careful construction of weights, these works show that multiple layers of transformers are expressive enough to simulate iterations of gradient descent.

Hence, the performance of LLMs in various NLP tasks depends significantly onthecrucial roleplayedbytheattention mechanism with thesoftmaxunit.