Related papers: On Contrastive Representations of Stochastic Processes

On Contrastive Representations of Stochastic Processes

URL: http://arxiv.org/abs/2106.10052v1
Date: Fri, 18 Jun 2021 11:00:24 GMT
Title: On Contrastive Representations of Stochastic Processes
Authors: Emile Mathieu, Adam Foster, Yee Whye Teh
Abstract summary: Learning representations of processes is an emerging problem in machine learning. We show that our methods are effective for learning representations of periodic functions, 3D objects and dynamical processes.
Score: 53.21653429290478
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Learning representations of stochastic processes is an emerging problem in machine learning with applications from meta-learning to physical object models to time series. Typical methods rely on exact reconstruction of observations, but this approach breaks down as observations become high-dimensional or noise distributions become complex. To address this, we propose a unifying framework for learning contrastive representations of stochastic processes (CRESP) that does away with exact reconstruction. We dissect potential use cases for stochastic process representations, and propose methods that accommodate each. Empirically, we show that our methods are effective for learning representations of periodic functions, 3D objects and dynamical processes. Our methods tolerate noisy high-dimensional observations better than traditional approaches, and the learned representations transfer to a range of downstream tasks.

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