3 citations · 4 across the 7 of their papers we have counts for
3 papers · 1 filter
Efficient Analytic Uncertainty Quantification for Multi-Modal Regression
Kun Jin, James Harrison, Jiawei Li +8
Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning. Existing UQ methods for regression tasks mainly operate under the assumption that the c…
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification
Randolph Linderman, Jingyang Zhang, Nathan Inkawhich +2
Machine learning methods must be trusted to make appropriate decisions in real-world environments, even when faced with out-of-distribution (OOD) samples. Many current approaches s…
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments
Jingyang Zhang, Nathan Inkawhich, Randolph Linderman +2
Many real-world scenarios in which DNN-based recognition systems are deployed have inherently fine-grained attributes (e.g., bird-species recognition, medical image classification)…