Sequential Pattern Retrieval: New Representations Inspired by Non-equilibrium Physics and Associative Memory Models
Matthew Du ⋅ Agnish Kumar Behera ⋅ Suriyanarayanan Vaikuntanathan
Abstract
Generating a temporal sequence of outputs from a single output has broad relevance, including in neuroscience and machine learning.Inspired by ideas in non-equilibrium physics and modern associative memory models, we demonstrate new representations of sequence recall. Our findings provide potential strategies to improve the learning of temporal data in state-of-the-art neural networks.
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