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Extracting temporal relationships over a range of scales is a hallmark ofhuman perception and cognition -- and thus it is a critical feature of machinelearning applied to real-world problems. Neural networks are either plagued bythe exploding/vanishing gradient problem in recurrent neural networks (RNNs) ormust adjust their parameters to learn the relevant time scales (e.g., inLSTMs). This paper introduces DeepSITH, a network comprisingbiologically-inspired Scale-Invariant Temporal History (SITH) modules in serieswith dense connections between layers. SITH modules respond to their inputswith a geometrically-spaced set of time constants, enabling the DeepSITHnetwork to learn problems along a continuum of time-scales. We compare DeepSITHto LSTMs and other recent RNNs on several time series prediction and decodingtasks. DeepSITH achieves state-of-the-art performance on these problems.

DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales