We propose a new Stein self-repulsive dynamics for obtaining diversified samples from intractable un-normalized distributions.

Furthermore, [30, 31] pointed out to conduct CL with reconstruction regularization to achieve robust representations for downstream tasks. RINCE [15] (a short name of Robust InfoNCE) is a variant of InfoNCE contrastive loss that considers noise in false positive sample pairs.

Facedwithanunfamiliar experimental system, itisoftenimpossible toknowaprioriwhichquantities to measure in order to gain insight into the system's dynamics. Instead, one typically must rely onwhichevermeasurements arereadily observable ortechnically feasible, resulting inpartial measurements that fail to fully describe a system's important properties.