924 citations · 938 across the 6 of their papers we have counts for
Showing 2019Show all
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stat.ML2019★ 8 cited
Likelihood Ratios for Out-of-Distribution Detection
Jie Ren, Peter J. Liu, Emily Fertig +5
Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distri…
stat.ML2019★ 924 cited
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia, Emily Fertig, Jie Ren +6
Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful…
cs.LG2019★ 3 cited
Dueling Decoders: Regularizing Variational Autoencoder Latent Spaces
Bryan Seybold, Emily Fertig, Alex Alemi +1
Variational autoencoders learn unsupervised data representations, but these models frequently converge to minima that fail to preserve meaningful semantic information. For example,…