Neural networks (NNs) are the method of choice for building learning algorithms. They are
now being investigated for other numerical tasks such as solving high-dimensional partial …

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 …

It is unknown whether near-term quantum computers are advantageous for machine
learning tasks. In this work we address this question by trying to understand how powerful …

Although pretrained language models can be fine-tuned to produce state-of-the-art results
for a very wide range of language understanding tasks, the dynamics of this process are not …

Pruning the parameters of deep neural networks has generated intense interest due to
potential savings in time, memory and energy both during training and at test time. Recent …

Pre-trained language models can be fine-tuned to solve diverse NLP tasks, including in few-
shot settings. Thus fine-tuning allows the model to quickly pick up task-specific" skills," but …

Neural networks have achieved remarkable performance across various problem domains,
but their widespread applicability is hindered by inherent limitations such as overconfidence …

Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-
imbalance but the testing criterion requires good generalization on less frequent classes …

Generalization of deep networks has been of great interest in recent years, resulting in a
number of theoretically and empirically motivated complexity measures. However, most …

Recent works have cast some light on the mystery of why deep nets fit any data and
generalize despite being very overparametrized. This paper analyzes training and …