S2RMs: Spatially Structured Recurrent Modules

13 Jul 2020Nasim RahamanAnirudh GoyalMuhammad Waleed GondalManuel WuthrichStefan BauerYash SharmaYoshua BengioBernhard Schölkopf

Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalize well and are robust to changes in the input distribution. While methods that harness spatial and temporal structures find broad application, recent work has demonstrated the potential of models that leverage sparse and modular structure using an ensemble of sparingly interacting modules... (read more)

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