Dec. 7, 2020
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NeurIPS papers aim to improve understanding and robustness of machine learning algorithms

Jeremy Thomas/LLNL

The 34th Conference on Neural Information Processing Systems (NeurIPS) is featuring two papers advancing the reliability of deep learning for mission-critical applications at LLNL. The most prestigious machine learning conference in the world, NeurIPS began virtually on Dec. 6. The first paper describes a framework for understanding the effect of properties of training data on the generalization gap of machine learning algorithms. For the second NeurIPS paper, a team including LLNL’s Kailkhura and co-authors at Northeastern University, China’s Tsinghua University and the University of California, Los Angeles developed an automatic framework to obtain robustness guarantees of any deep neural network structure using Linear Relaxation-based Perturbation Analysis (LiRPA). Read more at LLNL News.