Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …

Abstract Model complexity is a fundamental problem in deep learning. In this paper, we
conduct a systematic overview of the latest studies on model complexity in deep learning …

Collecting data is one of the bottlenecks of Human-Computer Interaction (HCI) research.
Motivated by this, we explore the potential of large language models (LLMs) in generating …

Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

The classical development of neural networks has primarily focused on learning mappings
between finite dimensional Euclidean spaces or finite sets. We propose a generalization of …

This textbook establishes a theoretical framework for understanding deep learning models
of practical relevance. With an approach that borrows from theoretical physics, Roberts and …

Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode
information in spatiotemporal dynamics. Recently, deep SNNs trained directly have shown …

Plasticity, the ability of a neural network to quickly change its predictions in response to new
information, is essential for the adaptability and robustness of deep reinforcement learning …

In deep learning theory, the covariance matrix of the representations serves as aproxy to
examine the network's trainability. Motivated by the success of Transform-ers, we study the …

Optical imaging is commonly used for both scientific and technological applications across
industry and academia. In image sensing, a measurement, such as of an object's position or …