10 citations · 11 across the 4 of their papers we have counts for
4 papers
Dynamics Reveals Structure: Challenging the Linear Propagation Assumption
Hoyeon Chang, Bálint Mucsányi, Seong Joon Oh
Neural networks adapt through first-order parameter updates, yet it remains unclear whether such updates preserve logical coherence. We investigate the geometric limits of the Line…
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
Bálint Mucsányi, Michael Kirchhof, Seong Joon Oh
Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty qua…
Trustworthy Machine Learning
Bálint Mucsányi, Michael Kirchhof, Elisa Nguyen +2
As machine learning technology gets applied to actual products and solutions, new challenges have emerged. Models unexpectedly fail to generalize to small changes in the distributi…
URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates
Michael Kirchhof, Bálint Mucsányi, Seong Joon Oh +1
Representation learning has significantly driven the field to develop pretrained models that can act as a valuable starting point when transferring to new datasets. With the rising…