Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Selecting a suitable pretraining dataset is crucial for both general-domain (eg, GPT-3) and
domain-specific (eg, Codex) language models (LMs). We formalize this problem as selecting …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

Classical machine learning assumes that the training and test sets come from the same
distributions. Therefore, a model learned from the labeled training data is expected to …

Abstract Machine learning-based models of protein fitness typically learn from either
unlabeled, evolutionarily related sequences or variant sequences with experimentally …

The issue of distribution shifts is emerging as a critical concern in graph representation
learning. From the perspective of invariant learning and stable learning, a recently well …

Accurate degradation monitoring over battery life is indispensable for the safe and durable
operation of battery-powered applications. In this work, we extend conventional capacity …