1 code implementation • 27 Nov 2023 • Zeming Chen, Alejandro Hernández Cano, Angelika Romanou, Antoine Bonnet, Kyle Matoba, Francesco Salvi, Matteo Pagliardini, Simin Fan, Andreas Köpf, Amirkeivan Mohtashami, Alexandre Sallinen, Alireza Sakhaeirad, Vinitra Swamy, Igor Krawczuk, Deniz Bayazit, Axel Marmet, Syrielle Montariol, Mary-Anne Hartley, Martin Jaggi, Antoine Bosselut
Large language models (LLMs) can potentially democratize access to medical knowledge.
Ranked #1 on Multiple Choice Question Answering (MCQA) on MedMCQA (Dev Set (Acc-%) metric)
1 code implementation • 17 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.
1 code implementation • 29 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.
2 code implementations • 22 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.
no code implementations • 14 Dec 2021 • Shahar Avin, Haydn Belfield, Miles Brundage, Gretchen Krueger, Jasmine Wang, Adrian Weller, Markus Anderljung, Igor Krawczuk, David Krueger, Jonathan Lebensold, Tegan Maharaj, Noa Zilberman
The range of application of artificial intelligence (AI) is vast, as is the potential for harm.
no code implementations • 29 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.
no code implementations • 1 Jan 2021 • Igor Krawczuk, Pedro Abranches, Andreas Loukas, Volkan Cevher
We study the fundamental problem of graph generation.
no code implementations • 23 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.
no code implementations • 15 Apr 2020 • Miles Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, Tegan Maharaj, Pang Wei Koh, Sara Hooker, Jade Leung, Andrew Trask, Emma Bluemke, Jonathan Lebensbold, Cullen O'Keefe, Mark Koren, Théo Ryffel, JB Rubinovitz, Tamay Besiroglu, Federica Carugati, Jack Clark, Peter Eckersley, Sarah de Haas, Maritza Johnson, Ben Laurie, Alex Ingerman, Igor Krawczuk, Amanda Askell, Rosario Cammarota, Andrew Lohn, David Krueger, Charlotte Stix, Peter Henderson, Logan Graham, Carina Prunkl, Bianca Martin, Elizabeth Seger, Noa Zilberman, Seán Ó hÉigeartaigh, Frens Kroeger, Girish Sastry, Rebecca Kagan, Adrian Weller, Brian Tse, Elizabeth Barnes, Allan Dafoe, Paul Scharre, Ariel Herbert-Voss, Martijn Rasser, Shagun Sodhani, Carrick Flynn, Thomas Krendl Gilbert, Lisa Dyer, Saif Khan, Yoshua Bengio, Markus Anderljung
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development.
Computers and Society
no code implementations • 25 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.