10 citations · 20 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…