An important goal of AutoML is to automate-away the design of neural networks on new tasks in under-explored domains.

An important goal of AutoML is to automate-away the design of neural networks on new tasks in under-explored domains.

We perform an extensive evaluation on a broad class of problems: Gaussian modelling, K-nearest neighbors search, geometric deep learning, non-Euclidean embeddings and optimal transport theory.

We propose a new "Poisson flow" generative model (PFGM) that maps a uniform

Chaotic systems are characterized by strong instabilities. Small perturbations to the system, such as the initialization, lead to errors which accumulate during time-evolution, due to positive Lyapunov exponents.

Recently Transformer has been largely explored in tracking and shown state-of-the-art (SOT A) performance. However, existing efforts mainly focus on fusing and enhancing features generated by convolutional neural networks (CNNs). The potential of Transformer in representation learning remains under-explored.