← Search

Marc Howard

2 accepted papers

2022

A deep convolutional neural network that is invariant to time rescaling

ICML 2022spotlight

Human learners can readily understand speech, or a melody, when it is presented slower or faster than usual. This paper presents a deep CNN (SITHCon) that uses a logarithmically compressed temporal representation at each level. Because rescaling the time of the input results in a translation of $\lo…

2021

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

NeurIPS 2021poster

Extracting temporal relationships over a range of scales is a hallmark of human perception and cognition---and thus it is a critical feature of machine learning applied to real-world problems. Neural networks are either plagued by the exploding/vanishing gradient problem in recurrent neural network…