Mini-Batch Training along Convolution Windows for Representation Learning Based on Spike-Time-Dependent-Plasticity Rule

Author:

Shimmyo Yohei1,Okuyama Yuichi2

Affiliation:

1. The University of Aizu,Graduate School of Computer Science and Engineering,Fukushima,Japan

2. The University of Aizu,School of Computer Science and Engineering,Fukushima,Japan

Publisher

IEEE

Reference26 articles.

1. DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction

2. Optimal Distributed Online Prediction Using Mini-Batches;dekel;Tech Rep,2012

3. Sample size selection in optimization methods for machine learning

4. Don't Decay the Learning Rate, Increase the Batch Size;smith;ArXiv,2017

5. Slayer: Spike layer error reassignment in time;shrestha;Advances in Neural IInformation Processing Systems,2018

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