In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

This two-part comprehensive survey is devoted to a computing framework most commonly
known under the names Hyperdimensional Computing and Vector Symbolic Architectures …

Dual encoders perform retrieval by encoding documents and queries into dense low-
dimensional vectors, scoring each document by its inner product with the query. We …

Quantization based techniques are the current state-of-the-art for scaling maximum inner
product search to massive databases. Traditional approaches to quantization aim to …

High-dimensional nonlinear state estimation is at the heart of inertial-aided navigation
systems (INS). Traditional methods usually rely on good initialization and find difficulty in …

Recent work has developed methods for learning deep network classifiers that are\emph
{provably} robust to norm-bounded adversarial perturbation; however, these methods are …

In this monograph, we have attempted to summarize the actively develo** field of
statistical learning with sparsity. A sparse statistical model is one having only a small …

Image hashing is a principled approximate nearest neighbor approach to find similar items
to a query in a large collection of images. Hashing aims to learn a binary-output function that …

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …

This book provides an introduction to the mathematical and algorithmic foundations of data
science, including machine learning, high-dimensional geometry, and analysis of large …