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2. Likelihood ratios for out-of-distribution detection;ren;NeurIPS,2019
3. Dropout as a bayesian approximation: Representing model uncertainty in deep learning;gal;ICML,2016
4. Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting;rasul;ICML,2021
5. Selective classification for deep neural networks;geifman;NeurIPS,2017