Language models (LMs) are pretrained on diverse data sources, including news, discussion
forums, books, and online encyclopedias. A significant portion of this data includes opinions …

The recent advances in neural language models have also been successfully applied to the
field of chemistry, offering generative solutions for classical problems in molecular design …

Abstract Background and Objective: As deep learning faces a reproducibility crisis and
studies on deep learning applied to neuroimaging are contaminated by methodological …

Neural forecasting of spatiotemporal time series drives both research and industrial
innovation in several relevant application domains. Graph neural networks (GNNs) are often …

Evaluating the factual consistency of automatically generated summaries is essential for the
progress and adoption of reliable summarization systems. Despite recent advances, existing …

An extractive rationale explains a language model's (LM's) prediction on a given task
instance by highlighting the text inputs that most influenced the prediction. Ideally, rationale …

Normalizing Flows are generative models that directly maximize the likelihood. Previously,
the design of normalizing flows was largely constrained by the need for analytical …

Predicting the impact of treatments from ob-servational data only still represents a major
challenge despite recent significant advances in time series modeling. Treatment …

Artificial intelligence and machine learning have shown great promise in their ability to
accelerate novel materials discovery. As researchers and domain scientists seek to unify …

Zoobot is a Python package for measuring the detailed appearance of galaxies in telescope
images using deep learning. Zoobot is aimed at astronomers who want to solve a galaxy …