Search Results for author: Nathan Grinsztajn

Found 19 papers, 6 papers with code

Command A: An Enterprise-Ready Large Language Model

no code implementations1 Apr 2025 Team Cohere, Aakanksha, Arash Ahmadian, Marwan Ahmed, Jay Alammar, Yazeed Alnumay, Sophia Althammer, Arkady Arkhangorodsky, Viraat Aryabumi, Dennis Aumiller, Raphaël Avalos, Zahara Aviv, Sammie Bae, Saurabh Baji, Alexandre Barbet, Max Bartolo, Björn Bebensee, Neeral Beladia, Walter Beller-Morales, Alexandre Bérard, Andrew Berneshawi, Anna Bialas, Phil Blunsom, Matt Bobkin, Adi Bongale, Sam Braun, Maxime Brunet, Samuel Cahyawijaya, David Cairuz, Jon Ander Campos, Cassie Cao, Kris Cao, Roman Castagné, Julián Cendrero, Leila Chan Currie, Yash Chandak, Diane Chang, Giannis Chatziveroglou, Hongyu Chen, Claire Cheng, Alexis Chevalier, Justin T. Chiu, Eugene Cho, Eugene Choi, Eujeong Choi, Tim Chung, Volkan Cirik, Ana Cismaru, Pierre Clavier, Henry Conklin, Lucas Crawhall-Stein, Devon Crouse, Andres Felipe Cruz-Salinas, Ben Cyrus, Daniel D'souza, Hugo Dalla-torre, John Dang, William Darling, Omar Darwiche Domingues, Saurabh Dash, Antoine Debugne, Théo Dehaze, Shaan Desai, Joan Devassy, Rishit Dholakia, Kyle Duffy, Ali Edalati, Ace Eldeib, Abdullah Elkady, Sarah Elsharkawy, Irem Ergün, Beyza Ermis, Marzieh Fadaee, Boyu Fan, Lucas Fayoux, Yannis Flet-Berliac, Nick Frosst, Matthias Gallé, Wojciech Galuba, Utsav Garg, Matthieu Geist, Mohammad Gheshlaghi Azar, Seraphina Goldfarb-Tarrant, Tomas Goldsack, Aidan Gomez, Victor Machado Gonzaga, Nithya Govindarajan, Manoj Govindassamy, Nathan Grinsztajn, Nikolas Gritsch, Patrick Gu, Shangmin Guo, Kilian Haefeli, Rod Hajjar, Tim Hawes, Jingyi He, Sebastian Hofstätter, Sungjin Hong, Sara Hooker, Tom Hosking, Stephanie Howe, Eric Hu, Renjie Huang, Hemant Jain, Ritika Jain, Nick Jakobi, Madeline Jenkins, JJ Jordan, Dhruti Joshi, Jason Jung, Trushant Kalyanpur, Siddhartha Rao Kamalakara, Julia Kedrzycki, Gokce Keskin, Edward Kim, Joon Kim, Wei-Yin Ko, Tom Kocmi, Michael Kozakov, Wojciech Kryściński, Arnav Kumar Jain, Komal Kumar Teru, Sander Land, Michael Lasby, Olivia Lasche, Justin Lee, Patrick Lewis, Jeffrey Li, Jonathan Li, Hangyu Lin, Acyr Locatelli, Kevin Luong, Raymond Ma, Lukas Mach, Marina Machado, Joanne Magbitang, Brenda Malacara Lopez, Aryan Mann, Kelly Marchisio, Olivia Markham, Alexandre Matton, Alex McKinney, Dominic McLoughlin, Jozef Mokry, Adrien Morisot, Autumn Moulder, Harry Moynehan, Maximilian Mozes, Vivek Muppalla, Lidiya Murakhovska, Hemangani Nagarajan, Alekhya Nandula, Hisham Nasir, Shauna Nehra, Josh Netto-Rosen, Daniel Ohashi, James Owers-Bardsley, Jason Ozuzu, Dennis Padilla, Gloria Park, Sam Passaglia, Jeremy Pekmez, Laura Penstone, Aleksandra Piktus, Case Ploeg, Andrew Poulton, Youran Qi, Shubha Raghvendra, Miguel Ramos, Ekagra Ranjan, Pierre Richemond, Cécile Robert-Michon, Aurélien Rodriguez, Sudip Roy, Laura Ruis, Louise Rust, Anubhav Sachan, Alejandro Salamanca, Kailash Karthik Saravanakumar, Isha Satyakam, Alice Schoenauer Sebag, Priyanka Sen, Sholeh Sepehri, Preethi Seshadri, Ye Shen, Tom Sherborne, Sylvie Chang Shi, Sanal Shivaprasad, Vladyslav Shmyhlo, Anirudh Shrinivason, Inna Shteinbuk, Amir Shukayev, Mathieu Simard, Ella Snyder, Ava Spataru, Victoria Spooner, Trisha Starostina, Florian Strub, Yixuan Su, Jimin Sun, Dwarak Talupuru, Eugene Tarassov, Elena Tommasone, Jennifer Tracey, Billy Trend, Evren Tumer, Ahmet Üstün, Bharat Venkitesh, David Venuto, Pat Verga, Maxime Voisin, Alex Wang, Donglu Wang, Shijian Wang, Edmond Wen, Naomi White, Jesse Willman, Marysia Winkels, Chen Xia, Jessica Xie, Minjie Xu, Bowen Yang, Tan Yi-Chern, Ivan Zhang, Zhenyu Zhao, Zhoujie Zhao

In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases.

Language Modeling Language Modelling +2

Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking

no code implementations24 Feb 2025 Shikha Surana, Nathan Grinsztajn, Timothy Atkinson, Paul Duckworth, Thomas D. Barrett

Machine learning methods can automate the in silico design of biological sequences, aiming to reduce costs and accelerate medical research.

Benchmarking

Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs

1 code implementation29 Nov 2023 Andries Smit, Paul Duckworth, Nathan Grinsztajn, Thomas D. Barrett, Arnu Pretorius

In this context, multi-agent debate (MAD) has emerged as a promising strategy for enhancing the truthfulness of LLMs.

Benchmarking

Combinatorial Optimization with Policy Adaptation using Latent Space Search

1 code implementation NeurIPS 2023 Felix Chalumeau, Shikha Surana, Clement Bonnet, Nathan Grinsztajn, Arnu Pretorius, Alexandre Laterre, Thomas D. Barrett

Combinatorial Optimization underpins many real-world applications and yet, designing performant algorithms to solve these complex, typically NP-hard, problems remains a significant research challenge.

Benchmarking Combinatorial Optimization +3

Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization

1 code implementation NeurIPS 2023 Nathan Grinsztajn, Daniel Furelos-Blanco, Shikha Surana, Clément Bonnet, Thomas D. Barrett

Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as it removes the need for expert knowledge or pre-solved instances.

All Combinatorial Optimization +4

Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round

no code implementations4 Aug 2022 Manh Hung Nguyen, Lisheng Sun, Nathan Grinsztajn, Isabelle Guyon

With the lessons learned from the first round and the feedback from the participants, we have designed the second round of our challenge with a new protocol and a new meta-dataset.

AutoML Meta-Learning

Low-Rank Projections of GCNs Laplacian

no code implementations ICLR Workshop GTRL 2021 Nathan Grinsztajn, Philippe Preux, Edouard Oyallon

In this work, we study the behavior of standard models for community detection under spectral manipulations.

Community Detection

Interferometric Graph Transform for Community Labeling

no code implementations4 Jun 2021 Nathan Grinsztajn, Louis Leconte, Philippe Preux, Edouard Oyallon

We present a new approach for learning unsupervised node representations in community graphs.

A spectral perspective on GCNs

no code implementations1 Jan 2021 Nathan Grinsztajn, Philippe Preux, Edouard Oyallon

In this work, we study the behavior of standard GCNs under spectral manipulations.

Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling

1 code implementation9 Nov 2020 Nathan Grinsztajn, Olivier Beaumont, Emmanuel Jeannot, Philippe Preux

In this paper, we propose a reinforcement learning approach to solve a realistic scheduling problem, and apply it to an algorithm commonly executed in the high performance computing community, the Cholesky factorization.

Combinatorial Optimization Deep Reinforcement Learning +3

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