71 citations · 112 across the 24 of their papers we have counts for
Showing 2026Show all
2 papers · 1 filter
stat.ML2026
Quantifying Epistemic Uncertainty in Diffusion Models
Aditi Gupta, Raphael A. Meyer, Yotam Yaniv +2
To ensure high quality outputs, it is important to quantify the epistemic uncertainty of diffusion models. Existing methods are often unreliable because they mix epistemic and alea…
cs.LG2026
PRISM: Distribution-free Adaptive Computation of Matrix Functions for Accelerating Neural Network Training
Shenghao Yang, Zhichao Wang, Oleg Balabanov +2
Matrix functions such as square root, inverse roots, and orthogonalization play a central role in preconditioned gradient methods for neural network training. This has motivated th…