14 citations · 18 across the 6 of their papers we have counts for
6 papers
Differentiable Rendering with Reparameterized Volume Sampling
Nikita Morozov, Denis Rakitin, Oleg Desheulin +2
In view synthesis, a neural radiance field approximates underlying density and radiance fields based on a sparse set of scene pictures. To generate a pixel of a novel view, it marc…
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces
Kirill Struminsky, Artyom Gadetsky, Denis Rakitin +2
Structured latent variables allow incorporating meaningful prior knowledge into deep learning models. However, learning with such variables remains challenging because of their dis…
Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution
Artyom Gadetsky, Kirill Struminsky, Christopher Robinson +2
Learning models with discrete latent variables using stochastic gradient descent remains a challenge due to the high variance of gradient estimates. Modern variance reduction techn…
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin
We study consistency properties of machine learning methods based on minimizing convex surrogates. We extend the recent framework of Osokin et al. (2017) for the quantitative analy…
The Deep Weight Prior
Andrei Atanov, Arsenii Ashukha, Kirill Struminsky +2
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 dist…
Robust Variational Inference
Michael Figurnov, Kirill Struminsky, Dmitry Vetrov
Variational inference is a powerful tool for approximate inference. However, it mainly focuses on the evidence lower bound as variational objective and the development of other mea…