all AI news
Set-Based Training for Neural Network Verification
April 22, 2024, 4:43 a.m. | Lukas Koller, Tobias Ladner, Matthias Althoff
cs.LG updates on arXiv.org arxiv.org
Abstract: Neural networks are vulnerable to adversarial attacks, i.e., small input perturbations can significantly affect the outputs of a neural network. In safety-critical environments, the inputs often contain noisy sensor data; hence, in this case, neural networks that are robust against input perturbations are required. To ensure safety, the robustness of a neural network must be formally verified. However, training and formally verifying robust neural networks is challenging. We address both of these challenges by employing, …
abstract adversarial adversarial attacks arxiv attacks case cs.cr cs.lg cs.lo data environments inputs network networks neural network neural networks robust robustness safety safety-critical sensor set small training type verification vulnerable
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US
Research Engineer
@ Allora Labs | Remote
Ecosystem Manager
@ Allora Labs | Remote
Founding AI Engineer, Agents
@ Occam AI | New York