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NeurIPS Features LLNL Papers and Software December 07, 2020

The 34th Conference on Neural Information Processing Systems (NeurIPS) features two LLNL papers advancing the reliability of deep learning for the Lab’s mission-critical applications. The most prestigious machine learning conference in the world, NeurIPS began virtually on December 6. The first paper describes a framework for understanding the effect of properties of training data on the generalization gap of machine learning (ML) algorithms—the difference between a model’s observed performance during training versus its “ground-truth” performance in the real world. The second NeurIPS paper introduces an automatic framework to obtain robustness guarantees of any deep neural network structure using the open source Linear Relaxation-based Perturbation Analysis (LiRPA) repo. Developed with colleagues at Northeastern University, China’s Tsinghua University, and UCLA, LiRPA algorithms can provide guaranteed upper and lower bounds for a neural network function with perturbed inputs.