49 citations · 150 across the 27 of their papers we have counts for
39 papers
Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed
Hao Cheng, Pu Zhao, Yize Li +4
Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full…
Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions
Evan R. Antoniuk, Peggy Li, Bhavya Kailkhura +1
One of the grand challenges of utilizing machine learning for the discovery of innovative new polymers lies in the difficulty of accurately representing the complex structures of p…
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices
Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura +1
The prediction accuracy of the deep neural networks (DNNs) after deployment at the edge can suffer with time due to shifts in the distribution of the new data. To improve robustnes…
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks
Fan Wu, Linyi Li, Chejian Xu +5
As reinforcement learning (RL) has achieved near human-level performance in a variety of tasks, its robustness has raised great attention. While a vast body of research has explore…
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
Jiachen Sun, Qingzhao Zhang, Bhavya Kailkhura +3
Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications. However, their robustness against corruptions is le…
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen +1
Unsupervised domain adaptation (UDA) enables cross-domain learning without target domain labels by transferring knowledge from a labeled source domain whose distribution differs fr…