vGraph: A Generative Model for Joint Community Detection and Node Representation Learning. - Fan-Yun Sun; Meng Qu; Jordan Hoffmann; Chin-Wei Huang; Jian Tang 005 (NeurIPS, 2019)

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. - Fan-Yun Sun; Jordan Hoffmann; Jian Tang 005 (CoRR, 2019)

Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures. - Jordan Hoffmann; Louis Maestrati; Yoshihide Sawada; Jian Tang 005; Jean Michel D. Sellier; Yoshua Bengio (CoRR, 2019)

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. - Fan-Yun Sun; Jordan Hoffmann; Vikas Verma; Jian Tang 005 (ICLR, 2020)

AlgebraNets. - Jordan Hoffmann; Simon Schmitt; Simon Osindero; Karen Simonyan; Erich Elsen (CoRR, 2020)

Recurrent Independent Mechanisms. - Anirudh Goyal; Alex Lamb; Jordan Hoffmann; Shagun Sodhani; Sergey Levine; Yoshua Bengio; Bernhard Schölkopf (ICLR, 2021)

Scaling Language Models: Methods, Analysis & Insights from Training Gopher. - Jack W. Rae; Sebastian Borgeaud; Trevor Cai; Katie Millican; Jordan Hoffmann; H. Francis Song; John Aslanides; Sarah Henderson; Roman Ring; Susannah Young; Eliza Rutherford; Tom Hennigan; et al. (CoRR, 2021)

A Systematic Investigation of Commonsense Knowledge in Large Language Models. - Xiang Lorraine Li; Adhiguna Kuncoro; Jordan Hoffmann; Cyprien de Masson d'Autume; Phil Blunsom; Aida Nematzadeh (EMNLP, 2022)

Improving Language Models by Retrieving from Trillions of Tokens. - Sebastian Borgeaud; Arthur Mensch; Jordan Hoffmann; Trevor Cai; Eliza Rutherford; Katie Millican; George van den Driessche 002; Jean-Baptiste Lespiau; Bogdan Damoc; Aidan Clark; Diego de Las Casas; Aurelia Guy; et al. (ICML, 2022)

Unified Scaling Laws for Routed Language Models. - Aidan Clark; Diego de Las Casas; Aurelia Guy; Arthur Mensch; Michela Paganini; Jordan Hoffmann; Bogdan Damoc; Blake A. Hechtman; Trevor Cai; Sebastian Borgeaud; George van den Driessche 002; Eliza Rutherford; et al. (ICML, 2022)

An empirical analysis of compute-optimal large language model training. - Jordan Hoffmann; Sebastian Borgeaud; Arthur Mensch; Elena Buchatskaya; Trevor Cai; Eliza Rutherford; Diego de Las Casas; Lisa Anne Hendricks; Johannes Welbl; Aidan Clark; Tom Hennigan; Eric Noland; et al. (NeurIPS, 2022)

Unified Scaling Laws for Routed Language Models. - Aidan Clark; Diego de Las Casas; Aurelia Guy; Arthur Mensch; Michela Paganini; Jordan Hoffmann; Bogdan Damoc; Blake A. Hechtman; Trevor Cai; Sebastian Borgeaud; George van den Driessche 002; Eliza Rutherford; et al. (CoRR, 2022)

Training Compute-Optimal Large Language Models. - Jordan Hoffmann; Sebastian Borgeaud; Arthur Mensch; Elena Buchatskaya; Trevor Cai; Eliza Rutherford; Diego de Las Casas; Lisa Anne Hendricks; Johannes Welbl; Aidan Clark; Tom Hennigan; Eric Noland; et al. (CoRR, 2022)

Policy composition in reinforcement learning via multi-objective policy optimization. - Shruti Mishra; Ankit Anand; Jordan Hoffmann; Nicolas Heess; Martin A. Riedmiller; Abbas Abdolmaleki; Doina Precup (CoRR, 2023)