Search Results for author: Igor Krawczuk

Found 10 papers, 4 papers with code

Distributed Extra-gradient with Optimal Complexity and Communication Guarantees

1 code implementation17 Aug 2023 Ali Ramezani-Kebrya, Kimon Antonakopoulos, Igor Krawczuk, Justin Deschenaux, Volkan Cevher

We consider monotone variational inequality (VI) problems in multi-GPU settings where multiple processors/workers/clients have access to local stochastic dual vectors.

DiGress: Discrete Denoising diffusion for graph generation

1 code implementation29 Sep 2022 Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, Pascal Frossard

This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes.

Denoising Edge Classification +1

Proximal Point Imitation Learning

2 code implementations22 Sep 2022 Luca Viano, Angeliki Kamoutsi, Gergely Neu, Igor Krawczuk, Volkan Cevher

Thanks to PPM, we avoid nested policy evaluation and cost updates for online IL appearing in the prior literature.

Imitation Learning

On the benefits of deep RL in accelerated MRI sampling

no code implementations29 Sep 2021 Thomas Sanchez, Igor Krawczuk, Volkan Cevher

Deep learning approaches have shown great promise in accelerating magnetic resonance imaging (MRI), by reconstructing high quality images from highly undersampled data.

Reinforcement Learning (RL)

Uncertainty-Driven Adaptive Sampling via GANs

no code implementations23 Oct 2020 Thomas Sanchez, Igor Krawczuk, Zhaodong Sun, Volkan Cevher

We propose an adaptive sampling method for the linear model, driven by the uncertainty estimation with a generative adversarial network (GAN) model.

Generative Adversarial Network SSIM

Closed loop deep Bayesian inversion: Uncertainty driven acquisition for fast MRI

no code implementations25 Sep 2019 Thomas Sanchez, Igor Krawczuk, Zhaodong Sun, Volkan Cevher

This work proposes a closed-loop, uncertainty-driven adaptive sampling frame- work (CLUDAS) for accelerating magnetic resonance imaging (MRI) via deep Bayesian inversion.


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