90 citations · 184 across the 8 of their papers we have counts for
8 papers
Evolutionary approaches to explainable machine learning
Ryan Zhou, Ting Hu
Machine learning models are increasingly being used in critical sectors, but their black-box nature has raised concerns about accountability and trust. The field of explainable art…
Symbolic Learning to Optimize: Towards Interpretability and Scalability
Wenqing Zheng, Tianlong Chen, Ting-Kuei Hu +1
Recent studies on Learning to Optimize (L2O) suggest a promising path to automating and accelerating the optimization procedure for complicated tasks. Existing L2O models parameter…
Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks
Ting-Kuei Hu, Fernando Gama, Tianlong Chen +4
In this paper, we present a perception-action-communication loop design using Vision-based Graph Aggregation and Inference (VGAI). This multi-agent decentralized learning-to-contro…
Undistillable: Making A Nasty Teacher That CANNOT teach students
Haoyu Ma, Tianlong Chen, Ting-Kuei Hu +3
Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models. However, in certain situa…
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
Haotao Wang, Tianlong Chen, Shupeng Gui +3
Adversarial training and its many variants substantially improve deep network robustness, yet at the cost of compromising standard accuracy. Moreover, the training process is heavy…
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference
Ting-Kuei Hu, Tianlong Chen, Haotao Wang +1
Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019). Such a…
VGAI: End-to-End Learning of Vision-Based Decentralized Controllers for Robot Swarms
Ting-Kuei Hu, Fernando Gama, Tianlong Chen +3
Decentralized coordination of a robot swarm requires addressing the tension between local perceptions and actions, and the accomplishment of a global objective. In this work, we pr…
Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference
Yue Wang, Jianghao Shen, Ting-Kuei Hu +5
State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widel…