Learning Behavior Representations Through Multi-Timescale Bootstrapping. (arXiv:2206.07041v1 [cs.LG])

Natural behavior consists of dynamics that are both unpredictable, can switch
suddenly, and unfold over many different timescales. While some success has
been found in building representations of behavior under constrained or
simplified task-based conditions, many of these models cannot be applied to
free and naturalistic settings due to the fact that they assume a single scale
of temporal dynamics. In this work, we introduce Bootstrap Across Multiple
Scales (BAMS), a multi-scale representation learning model for behavior: we
combine a …

arxiv behavior bootstrapping learning lg