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Sat Jun 15 12:30 AM -- 10:00 AM (KST)
Out-of-Distribution Detection Using Deep Likelihood Ratios
Sat Jun 15 12:30 AM -- 10:00 AM (KST)
Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection
Sat Jun 15 12:30 AM -- 10:00 AM (KST)
Stochastic Prototype Embeddings
Sat Jun 15 12:40 AM -- 01:30 AM (KST)
Robust training of conditional GANs from a few labels
Sat Jun 15 01:30 AM -- 02:00 AM (KST)
Keynote by Max Welling: A Nonparametric Bayesian Approach to Deep Learning (without GPs)
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Sat Jun 15 02:00 AM -- 03:00 AM (KST)
Poster Session 1 (all papers)
Sat Jun 15 03:00 AM -- 03:30 AM (KST)
Keynote by Kilian Weinberger: On Calibration and Fairness
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Sat Jun 15 03:30 AM -- 03:40 AM (KST)
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
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Sat Jun 15 03:50 AM -- 04:00 AM (KST)
How Can We Be So Dense? The Robustness of Highly Sparse Representations
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Sat Jun 15 04:00 AM -- 04:30 AM (KST)
Keynote by Suchi Saria: Safety Challenges with Black-Box Predictors and Novel Learning Approaches for Failure Proofing
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Sat Jun 15 06:10 AM -- 06:20 AM (KST)
Quality of Uncertainty Quantification for Bayesian Neural Network Inference
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Sat Jun 15 06:30 AM -- 07:00 AM (KST)
Keynote by Dawn Song: Adversarial Machine Learning: Challenges, Lessons, and Future Directions
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Sat Jun 15 07:30 AM -- 08:00 AM (KST)
Keynote by Terrance Boult: The Deep Unknown: on Open-set and Adversarial Examples in Deep Learning
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Sat Jun 15 09:00 AM -- 10:00 AM (KST)
Poster Session 2 (all papers)
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