Search Results for author:
Found 4 papers, 1 papers with code
Diffusion Models With Learned Adaptive Noise
Diffusion models have gained traction as powerful algorithms for synthesizing high-quality images.
Ranked #1 on
Density Estimation
on ImageNet 32x32
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
When these potentials further satisfy certain self-bounding properties, we show that they can be used to provide a convergence guarantee for Gradient Descent (GD) and SGD (even when the paths of GF and GD/SGD are quite far apart).
Model Selection's Disparate Impact in Real-World Deep Learning Applications
Algorithmic fairness has emphasized the role of biased data in automated decision outcomes.
Meta-Learning for Variational Inference
Variational inference (VI) plays an essential role in approximate Bayesian inference due to its computational efficiency and general applicability.
