The field of deep learning has witnessed significant progress, particularly in computer vision
(CV), natural language processing (NLP), and speech. The use of large-scale models …

Recent advances in deep learning have relied heavily on the use of large Transformers due
to their ability to learn at scale. However, the core building block of Transformers, the …

This survey describes probabilistic algorithms for linear algebraic computations, such as
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …

Large foundation models are becoming ubiquitous, but training them from scratch is
prohibitively expensive. Thus, efficiently adapting these powerful models to downstream …

The field of deep learning has witnessed significant progress in recent times, particularly in
areas such as computer vision (CV), natural language processing (NLP), and speech. The …

Overparameterized neural networks generalize well but are expensive to train. Ideally, one
would like to reduce their computational cost while retaining their generalization benefits …

We propose a novel neural network architecture, SwitchNet, for solving wave equation
based inverse scattering problems via providing maps between the scatterers and the …

Fast linear transforms are ubiquitous in machine learning, including the discrete Fourier
transform, discrete cosine transform, and other structured transformations such as …

This chapter offers a state-of-the-art analysis of boundary work and journalism. Physical
boundaries dictate how space is understood and creates complex impediments. Boundary …

A butterfly-based direct combined-field integral-equation (CFIE) solver for analyzing
scattering from electrically large, perfect electrically conducting objects is presented. The …