1 code implementation • 21 Feb 2023 • Nikita Morozov, Denis Rakitin, Oleg Desheulin, Dmitry Vetrov, Kirill Struminsky
To generate a pixel of a novel view, it marches a ray through the pixel and computes a weighted sum of radiance emitted from a dense set of ray points.
1 code implementation • NeurIPS 2021 • Kirill Struminsky, Artyom Gadetsky, Denis Rakitin, Danil Karpushkin, Dmitry Vetrov
Structured latent variables allow incorporating meaningful prior knowledge into deep learning models.
1 code implementation • 22 Nov 2019 • Artyom Gadetsky, Kirill Struminsky, Christopher Robinson, Novi Quadrianto, Dmitry Vetrov
Learning models with discrete latent variables using stochastic gradient descent remains a challenge due to the high variance of gradient estimates.
no code implementations • NeurIPS 2018 • Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin
We study consistency properties of machine learning methods based on minimizing convex surrogates.
2 code implementations • ICLR 2019 • Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry Vetrov, Max Welling
Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution.
no code implementations • 28 Nov 2016 • Michael Figurnov, Kirill Struminsky, Dmitry Vetrov
Variational inference is a powerful tool for approximate inference.