Novel gating mechanisms for temporal convolutional networks

Date

2021-09

Editor(s)

Advisor

Kozat, Süleyman Serdar

Supervisor

Co-Advisor

Co-Supervisor

Instructor

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Abstract

We investigate the sequential modeling problem and introduce a novel gating mechanism into the temporal convolutional network architectures. In particular, we propose the Gated Temporal Convolutional Network architecture with elaborately tailored gating mechanisms. In our implementation, we alter the way in which the gradients ow and avoid the vanishing or exploding gradient and the dead ReLU problems. The proposed GTCN architecture is able to model the irregularly sampled sequences as well. In our experiments, we show that the basic GTCN architecture is superior to the generic TCN architectures in various benchmark tasks requiring the modeling of long-term dependencies and irregular sampling intervals. Moreover, we achieve the state-of-the-art results on the permuted sequential MNIST and the sequential CIFAR10 benchmarks with the basic structure.

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Course

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Book Title

Degree Discipline

Electrical and Electronic Engineering

Degree Level

Master's

Degree Name

MS (Master of Science)

Citation

Published Version (Please cite this version)

Language

English

Type