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Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise.

, , , and . NeurIPS, page 10477-10486. (2018)

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How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios., , , , , , , , and . NeurIPS, (2022)The Trojan Detection Challenge., , , , , , , , , and 9 other author(s). NeurIPS (Competition and Demos), volume 220 of Proceedings of Machine Learning Research, page 279-291. PMLR, (2021)HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal., , , , , , , , , and 2 other author(s). CoRR, (2024)Scaling Out-of-Distribution Detection for Real-World Settings., , , , , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 8759-8773. PMLR, (2022)How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection., , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 15241-15254. PMLR, (2022)Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models, , , , , , , , , and 441 other author(s). (2022)cite arxiv:2206.04615Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-bench.PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures., , , , , , and . CVPR, page 16762-16771. IEEE, (2022)What Would Jiminy Cricket Do? Towards Agents That Behave Morally., , , , , , , , and . NeurIPS Datasets and Benchmarks, (2021)Measuring Massive Multitask Language Understanding., , , , , , and . ICLR, OpenReview.net, (2021)Deep Anomaly Detection with Outlier Exposure, , and . (2018)cite arxiv:1812.04606Comment: ICLR 2019; PyTorch code available at https://github.com/hendrycks/outlier-exposure.