A Dynamic Weight Quantization-Based Fault-tolerant Training Method for Ternary Memristive Neural Networks
Author:
Affiliation:
1. Hunan University,College of Computer Science and Electronic Engineering,Changsha,China
2. Guangdong University of Technology,School of Computer,Guangzhou,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10661305/10661306/10661325.pdf?arnumber=10661325
Reference20 articles.
1. Improving Image Recognition by Retrieving from Web-Scale Image-Text Data
2. Mixed-modality speech recognition and interaction using a wearable artificial throat
3. Natural language processing: state of the art, current trends and challenges
4. Ternary Weight Networks
5. ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions
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