187 citations · 575 across the 29 of their papers we have counts for
32 papers
Towards Real-time Text-driven Image Manipulation with Unconditional Diffusion Models
Nikita Starodubcev, Dmitry Baranchuk, Valentin Khrulkov +1
Recent advances in diffusion models enable many powerful instruments for image editing. One of these instruments is text-driven image manipulations: editing semantic attributes of…
Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts
Gleb Bazhenov, Denis Kuznedelev, Andrey Malinin +2
In reliable decision-making systems based on machine learning, models have to be robust to distributional shifts or provide the uncertainty of their predictions. In node-level prob…
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
Oleg Platonov, Denis Kuznedelev, Michael Diskin +2
Node classification is a classical graph machine learning task on which Graph Neural Networks (GNNs) have recently achieved strong results. However, it is often believed that stand…
Is This Loss Informative? Faster Text-to-Image Customization by Tracking Objective Dynamics
Anton Voronov, Mikhail Khoroshikh, Artem Babenko +1
Text-to-image generation models represent the next step of evolution in image synthesis, offering a natural way to achieve flexible yet fine-grained control over the result. One em…
TabDDPM: Modelling Tabular Data with Diffusion Models
Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev +1
Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities. Being the most prevalent in the comp…
Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond
Oleg Platonov, Denis Kuznedelev, Artem Babenko +1
Homophily is a graph property describing the tendency of edges to connect similar nodes; the opposite is called heterophily. It is often believed that heterophilous graphs are chal…