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Found 7 papers, 5 papers with code
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty quantification.
Pretrained Visual Uncertainties
Similar to standard pretraining this enables the zero-shot transfer of uncertainties learned on a large pretraining dataset to specialized downstream datasets.
Trustworthy Machine Learning
Collectively, we face a trustworthiness issue with the current machine learning technology.
Out-of-Distribution Generalization
Uncertainty Quantification
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
We prove that these distributions recover the correct posteriors of the data-generating process, including its level of aleatoric uncertainty, up to a rotation of the latent space.
A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning
We model images as directional von Mises-Fisher (vMF) distributions on the hypersphere that can reflect image-intrinsic uncertainties.
When are Post-hoc Conceptual Explanations Identifiable?
Interest in understanding and factorizing learned embedding spaces through conceptual explanations is steadily growing.
Root Cause Analysis in Lithium-Ion Battery Production with FMEA-Based Large-Scale Bayesian Network
The production of lithium-ion battery cells is characterized by a high degree of complexity due to numerous cause-effect relationships between process characteristics.
