Person
Igor Krawczuk
Related publications (5)
Graph generative deep learning models with an application to circuit topologies
Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison.Managing their complexity and facilitating their design drove part of the co-evolution of moder ...
EPFL2024DiGress: Discrete Denoising diffusion for graph generation
Pascal Frossard, Volkan Cevher, Igor Krawczuk, Bohan Wang, Clément Arthur Yvon Vignac
This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process that progressively edits graphs with noise, through the process of adding or ...
2023Proximal Point Imitation Learning
Volkan Cevher, Luca Viano, Igor Krawczuk, Angeliki Kamoutsi
This work develops new algorithms with rigorous efficiency guarantees for infinite horizon imitation learning (IL) with linear function approximation without restrictive coherence assumptions. We begin with the minimax formulation of the problem and then o ...
2022Multi-ReRAM synapses for artificial neural network training
Elmira Shahrabi, Igor Krawczuk, Yusuf Leblebici, Irem Boybat Kara, Carlo Ricciardi, Cecilia Giovinazzo, Evangelos Eleftheriou, Abu Sebastian
Metal-oxide-based resistive memory devices (ReRAM) are being actively researched as synaptic elements of neuromorphic co-processors for training deep neural networks (DNNs). However, device-level non-idealities are posing significant challenges. In this wo ...
IEEE2019Effect of metal buffer layer and thermal annealing on HfOx-based ReRAMs
Elmira Shahrabi, Igor Krawczuk, Yusuf Leblebici, Jury Sandrini, Behnoush Attarimashalkoubeh
In this paper, we investigate different methods and approaches in order to improve the electrical characteristics of Pt/HfOx/TiN ReRAM devices. We discuss the improvement of the ReRAM electrical characteristics after the insertion of a Hf and Ti buffer lay ...
Ieee2016