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cs.LG2026
Anti-Backdoor Coreset Selection via Cumulative Entropy
Qi Zhao, Christian Wressnegger
Recent training-time defenses against neural backdoors isolate a benign subset from poisoned training data, to learn a backdoor-free model from it. In this paper, we formulate this…
cs.LG2024
Holistic Adversarially Robust Pruning
Qi Zhao, Christian Wressnegger
Neural networks can be drastically shrunk in size by removing redundant parameters. While crucial for the deployment on resource-constraint hardware, oftentimes, compression comes…
cs.LG2021
BreakingBED -- Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks
Manoj Rohit Vemparala, Alexander Frickenstein, Nael Fasfous +8
Deploying convolutional neural networks (CNNs) for embedded applications presents many challenges in balancing resource-efficiency and task-related accuracy. These two aspects have…