2 citations · 2 across the 5 of their papers we have counts for
5 papers
Forget Superresolution, Sample Adaptively (when Path Tracing)
Martin Bálint, Corentin Salaün, Hans-Peter Seidel +1
Real-time path tracing increasingly operates under extremely low sampling budgets, often below one sample per pixel, as rendering complexity, resolution, and frame-rate requirement…
Multiple Importance Sampling for Stochastic Gradient Estimation
Corentin Salaün, Xingchang Huang, Iliyan Georgiev +2
We introduce a theoretical and practical framework for efficient importance sampling of mini-batch samples for gradient estimation from single and multiple probability distribution…
Online Importance Sampling for Stochastic Gradient Optimization
Corentin Salaün, Xingchang Huang, Iliyan Georgiev +2
Machine learning optimization often depends on stochastic gradient descent, where the precision of gradient estimation is vital for model performance. Gradients are calculated from…
Perceptual error optimization for Monte Carlo animation rendering
Miša Korać, Corentin Salaün, Iliyan Georgiev +4
Independently estimating pixel values in Monte Carlo rendering results in a perceptually sub-optimal white-noise distribution of error in image space. Recent works have shown that…
Regression-based Monte Carlo Integration
Corentin Salaün, Adrien Gruson, Binh-Son Hua +2
Monte Carlo integration is typically interpreted as an estimator of the expected value using stochastic samples. There exists an alternative interpretation in calculus where Monte…