Ting-Wu Chin

Orcid: 0000-0003-2953-0489

Affiliations:
  • Carnegie Mellon University, Department of Electrical and Computer Engineering, Pittsburgh, PA, USA


According to our database1, Ting-Wu Chin authored at least 22 papers between 2017 and 2023.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
QUIDAM: A Framework for Quantization-aware DNN Accelerator and Model Co-Exploration.
ACM Trans. Embed. Comput. Syst., March, 2023

2022
On Designing Resource-Constrained CNNs Efficiently.
PhD thesis, 2022

Play It Cool: Dynamic Shifting Prevents Thermal Throttling.
CoRR, 2022

ANT: Adapt Network Across Time for Efficient Video Processing.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021
Putting the "Machine" Back in Machine Learning for Engineering Students.
Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, 2021

Joslim: Joint Widths and Weights Optimization for Slimmable Neural Networks.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

Width Transfer: On the (In)variance of Width Optimization.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020
PareCO: Pareto-aware Channel Optimization for Slimmable Neural Networks.
CoRR, 2020

Improving the Adversarial Robustness of Transfer Learning via Noisy Feature Distillation.
CoRR, 2020

One Weight Bitwidth to Rule Them All.
Proceedings of the Computer Vision - ECCV 2020 Workshops, 2020

Towards Efficient Model Compression via Learned Global Ranking.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
LeGR: Filter Pruning via Learned Global Ranking.
CoRR, 2019

AdaScale: Towards Real-time Video Object Detection using Adaptive Scaling.
Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, 2019

FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference.
Proceedings of the 56th Annual Design Automation Conference 2019, 2019

Regularizing Activation Distribution for Training Binarized Deep Networks.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Domain-Specific Approximation for Object Detection.
IEEE Micro, 2018

Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks.
CoRR, 2018

Understanding the Impact of Label Granularity on CNN-Based Image Classification.
Proceedings of the 2018 IEEE International Conference on Data Mining Workshops, 2018

Designing adaptive neural networks for energy-constrained image classification.
Proceedings of the International Conference on Computer-Aided Design, 2018

2017
Flying IoT: Toward Low-Power Vision in the Sky.
IEEE Micro, 2017

Improving the accuracy of the leakage power estimation of embedded CPUs.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2017


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