Chang Song

Affiliations:
  • Duke University, Durham, NC, USA (PhD 2021)
  • University of Pittsburgh, Electrical and Computer Engineering Department, PA, USA


According to our database1, Chang Song authored at least 13 papers between 2016 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2021
Robustness Analysis and Improvement in Neural Networks and Neuromorphic Computing.
PhD thesis, 2021

2020
Improving Adversarial Robustness in Weight-quantized Neural Networks.
CoRR, 2020

Adversarial Attack: A New Threat to Smart Devices and How to Defend It.
IEEE Consumer Electron. Mag., 2020

2019
Feedback Learning for Improving the Robustness of Neural Networks.
Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

2018
Neuromorphic computing's yesterday, today, and tomorrow - an evolutional view.
Integr., 2018

Low-Power, Adaptive Neuromorphic Systems: Recent Progress and Future Directions.
IEEE J. Emerg. Sel. Topics Circuits Syst., 2018

MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks.
Proceedings of the 2018 IEEE Computer Society Annual Symposium on VLSI, 2018

Exploring the opportunity of implementing neuromorphic computing systems with spintronic devices.
Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition, 2018

2017
A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks.
CoRR, 2017

A quantization-aware regularized learning method in multilevel memristor-based neuromorphic computing system.
Proceedings of the IEEE 6th Non-Volatile Memory Systems and Applications Symposium, 2017

Low-power neuromorphic speech recognition engine with coarse-grain sparsity.
Proceedings of the 22nd Asia and South Pacific Design Automation Conference, 2017

2016
Exploring the optimal learning technique for IBM TrueNorth platform to overcome quantization loss.
Proceedings of the IEEE/ACM International Symposium on Nanoscale Architectures, 2016

Design techniques of eNVM-enabled neuromorphic computing systems.
Proceedings of the 34th IEEE International Conference on Computer Design, 2016


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