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Denis Rakitin

4 accepted papers

2026

GAS: Improving Discretization of Diffusion ODEs via Generalized Adversarial Solver

ICLR 2026poster

While diffusion models achieve state-of-the-art generation quality, they still suffer from computationally expensive sampling. Recent works address this issue with gradient-based optimization methods that distill a few-step ODE diffusion solver from the full sampling process, reducing the number of…

Cited by 0SourcecodeScholar
2026

One-step Optimal Transport via Regularized Distribution Matching Distillation

ICML 2026poster

Unpaired domain translation remains a challenging task due to the need of finding a balance between faithfulness and realism. In this paper, we propose a method called Regularized Distribution Matching Distillation (RDMD) that combines the best properties of Optimal Transport (OT) and diffusion-base…

Cited by 0SourceScholar
2024

Differentiable Rendering with Reparameterized Volume Sampling

AISTATS 2024poster

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 marches a ray through the pixel and computes a weighted sum of radiance emitted from a dense set of ray points. This renderi…

2021

Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces

NeurIPS 2021poster

Structured latent variables allow incorporating meaningful prior knowledge into deep learning models. However, learning with such variables remains challenging because of their discrete nature. Nowadays, the standard learning approach is to define a latent variable as a perturbed algorithm output an…