1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025
Disproving the Feasibility of Learned Confidence Calibration Under Binary Supervision: An Information-Theoretic Impossibility
Arjun S. Nair, Kristina P. Sinaga
We prove a fundamental impossibility theorem: neural networks cannot simultaneously learn well-calibrated confidence estimates with meaningful diversity when trained using binary c…
eess.IV2022★ 1 cited
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CT
Sam Ellis, Octavio E. Martinez Manzanera, Vasileios Baltatzis +6
GANs are able to model accurately the distribution of complex, high-dimensional datasets, e.g. images. This makes high-quality GANs useful for unsupervised anomaly detection in med…