Computer-aided drug discovery/design methods have played a major role in the
development of therapeutically important small molecules for over three decades. These …

Statistical learning theory was introduced in the late 1960's. Until the 1990's it was a purely
theoretical analysis of the problem of function estimation from a given collection of data. In …

The use of quantum computing for machine learning is among the most exciting prospective
applications of quantum technologies. However, machine learning tasks where data is …

The remarkable practical success of deep learning has revealed some major surprises from
a theoretical perspective. In particular, simple gradient methods easily find near-optimal …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

The introduction gives some context about what quantum machine learning is, how it got
established as a sub-discipline of quantum computing and which higher level approaches …

We study the performance of classical and quantum machine learning (ML) models in
predicting outcomes of physical experiments. The experiments depend on an input …

This paper deals with the use of emerging deep learning techniques in future wireless
communication networks. It will be shown that the data-driven approaches should not …

Deep learning (DL) creates impactful advances following a virtuous recipe: model
architecture search, creating large training data sets, and scaling computation. It is widely …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …