1k citations · 1.1k across the 7 of their papers we have counts for
13 papers
A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations
Yihan Pan, Zhenghang Xu, Jin Guang +10
We collaborate with a large teaching hospital in Shenzhen, China and build a high-fidelity simulation model for its ultrasound center to predict key performance metrics, including…
Understanding Robustness in Teacher-Student Setting: A New Perspective
Zhuolin Yang, Zhaoxi Chen, Tiffany Cai +3
Adversarial examples have appeared as a ubiquitous property of machine learning models where bounded adversarial perturbation could mislead the models to make arbitrarily incorrect…
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
Micah Goldblum, Dimitris Tsipras, Chulin Xie +6
As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve…
Compositional Generalization via Neural-Symbolic Stack Machines
Xinyun Chen, Chen Liang, Adams Wei Yu +2
Despite achieving tremendous success, existing deep learning models have exposed limitations in compositional generalization, the capability to learn compositional rules and apply…
Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis
Kavi Gupta, Peter Ebert Christensen, Xinyun Chen +1
The use of deep learning techniques has achieved significant progress for program synthesis from input-output examples. However, when the program semantics become more complex, it…
Spatiotemporal Attacks for Embodied Agents
Aishan Liu, Tairan Huang, Xianglong Liu +5
Adversarial attacks are valuable for providing insights into the blind-spots of deep learning models and help improve their robustness. Existing work on adversarial attacks have ma…