Large language models (LLMs) have been shown to be able to perform new tasks based on
a few demonstrations or natural language instructions. While these capabilities have led to …

Understanding relationships between sets is an important analysis task that has received
widespread attention in the visualization community. The major challenge in this context is the …

We present HiGlass, an open source visualization tool built on web technologies that
provides a rich interface for rapid, multiplex, and multiscale navigation of 2D genomic maps …

Deep neural networks excel at finding hierarchical representations that solve complex tasks
over large datasets. How can we humans understand these learned representations? In this …

Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are
a remarkably effective tool for sequence modeling that learn a dense black-box hidden …

… Recall that ru,P is the one-channel h × w featuremap of unit u in a convolutional generator,
where h × w is typically smaller than the image size. We want to know if a specific unit ru,P …

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical
repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report …

… Song, and H. Qu. Understanding hidden memories of recurrent neural networks. … Schick
and H. Schütze. It’s not just size that matters: Small language models are also few-shot learners. …

… H. Strobelt is a visiting research scientist at MIT. Funding: This work was supported by IBM
Research. Author contributions: The project was conceived and planned by PS and BH and …

The rapid improvement of language models has raised the specter of abuse of text generation
systems. This progress motivates the development of simple methods for detecting …