Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design.

While existing defenses often rely on regularization techniques to reduce information leakage, they remain vulnerable to recent attacks.

Widely available earbuds equipped with sensors (also known as earables) can be combined with a sleep event detection algorithm to offer a convenient alternative to laborious clinical tests for individuals suffering from sleep disorders. Although various solutions utilizing such devices have been proposed to detect sleep events, they ignore the fact that individuals often share sleeping spaces with roommates or couples. To address this issue, we introduce DreamCatcher, the first publicly available dataset for wearer-aware sleep event algorithm development on earables.

Evidential Deep Learning (EDL), grounded in Evidence Theory and Subjective Logic (SL), provides a robust framework to estimate uncertainty for out-of-distribution (OOD) detection alongside traditional classification probabilities.

Automating outlier data generation has thus been a long-desired alternative.