Artificial intelligence has recently experienced remarkable advances, fueled by large
models, vast datasets, accelerated hardware, and, last but not least, the transformative …

Diffusion probabilistic models (DPMs) have exhibited excellent performance for high-fidelity
image generation while suffering from inefficient sampling. Recent works accelerate the …

A meaningful solution to an inversion problem should be composed of the preferred
inversion model and its uncertainty and resolution estimates. The model uncertainty …

Incorporating second-order curvature information into machine learning optimization
algorithms can be subtle, and doing so naïvely can lead to high per-iteration costs …

We propose a new formulation for pruning convolutional kernels in neural networks to
enable efficient inference. We interleave greedy criteria-based pruning with fine-tuning by …

Training machine learning models on massive datasets incurs substantial computational
costs. To alleviate such costs, there has been a sustained effort to develop data-efficient …

Parameter-specific adaptive learning rate methods are computationally efficient ways to
reduce the ill-conditioning problems encountered when training large deep networks …

Randomized algorithms for very large matrix problems have received a great deal of
attention in recent years. Much of this work was motivated by problems in large-scale data …

Since the early 1980. the first author has taught a graduate course in numerical linear
algebra at MIT and Cornell. The alumni of this course, now numbering in the hundreds, have …

Parameter Estimation and Inverse Problems, Third Edition, is structured around a course at
New Mexico Tech and is designed to be accessible to typical graduate students in the …