10 citations · 20 across the 7 of their papers we have counts for
7 papers
Real-Time Fully Unsupervised Domain Adaptation for Lane Detection in Autonomous Driving
Kshitij Bhardwaj, Zishen Wan, Arijit Raychowdhury +1
While deep neural networks are being utilized heavily for autonomous driving, they need to be adapted to new unseen environmental conditions for which they were not trained. We foc…
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…
Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations
Hao Cheng, Kaidi Xu, Chenan Wang +3
To tackle the susceptibility of deep neural networks to adversarial examples, the adversarial training has been proposed which provides a notion of security through an inner maximi…
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing
Cheng Chen, Bhavya Kailkhura, Ryan Goldhahn +1
Federated learning is an emerging data-private distributed learning framework, which, however, is vulnerable to adversarial attacks. Although several heuristic defenses are propose…
Hybrid Information-driven Multi-agent Reinforcement Learning
William A. Dawson, Ruben Glatt, Edward Rusu +2
Information theoretic sensor management approaches are an ideal solution to state estimation problems when considering the optimal control of multi-agent systems, however they are…
Byzantine-Resilient Locally Optimum Detection Using Collaborative Autonomous Networks
Bhavya Kailkhura, Priyadip Ray, Deepak Rajan +3
In this paper, we propose a locally optimum detection (LOD) scheme for detecting a weak radioactive source buried in background clutter. We develop a decentralized algorithm, based…
Robust Decentralized Learning Using ADMM with Unreliable Agents
Qunwei Li, Bhavya Kailkhura, Ryan Goldhahn +2
Many machine learning problems can be formulated as consensus optimization problems which can be solved efficiently via a cooperative multi-agent system. However, the agents in the…