However, we observe that how to select samples for labelling also significantly impacts performance, particularly under extremely low-budget settings. The sample selection task in SSL has been under-explored for a long time.

Contrastive PhenoMolecular Retrieval, which consists of zero-shot molecular structure identification conditioned on phenomic experiments. We assess challenges in multi-modal learning of phenomics and molecular modalities such as experimental batch effect, inactive molecule perturbations, and encoding perturbation concentration.

This makes HMC one of the most powerful sampling algorithms to date [36, 23].

Collecting large quantities of high-quality data can be prohibitively expensive or impractical, and a bottleneck in machine learning.

When artificial agents are jointly trained to perform collaborative tasks using a communication channel, they develop opaque goal-oriented communication protocols.

Time Series Classification (TSC) encompasses two settings: classifying entire sequences or classifying segmented subsequences.

Elliptical pays attention to more relevant information.