Search Results for author: Jordan Hoffmann

Found 12 papers, 7 papers with code

Policy composition in reinforcement learning via multi-objective policy optimization

no code implementations29 Aug 2023 Shruti Mishra, Ankit Anand, Jordan Hoffmann, Nicolas Heess, Martin Riedmiller, Abbas Abdolmaleki, Doina Precup

In two domains with continuous observation and action spaces, our agents successfully compose teacher policies in sequence and in parallel, and are also able to further extend the policies of the teachers in order to solve the task.


Scaling Language Models: Methods, Analysis & Insights from Training Gopher

no code implementations NA 2021 Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent SIfre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d'Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew Johnson, Blake Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Ed Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, Geoffrey Irving

Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.

 Ranked #1 on College Mathematics on BIG-bench (using extra training data)

Abstract Algebra Anachronisms +133

A Systematic Investigation of Commonsense Knowledge in Large Language Models

no code implementations31 Oct 2021 Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann, Cyprien de Masson d'Autume, Phil Blunsom, Aida Nematzadeh

Language models (LMs) trained on large amounts of data have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup.


1 code implementation12 Jun 2020 Jordan Hoffmann, Simon Schmitt, Simon Osindero, Karen Simonyan, Erich Elsen

Neural networks have historically been built layerwise from the set of functions in ${f: \mathbb{R}^n \to \mathbb{R}^m }$, i. e. with activations and weights/parameters represented by real numbers, $\mathbb{R}$.

Image Classification Language Modelling

Recurrent Independent Mechanisms

3 code implementations ICLR 2021 Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf

Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying causes.

Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures

1 code implementation3 Sep 2019 Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada, Jian Tang, Jean Michel Sellier, Yoshua Bengio

We present a method to encode and decode the position of atoms in 3-D molecules from a dataset of nearly 50, 000 stable crystal unit cells that vary from containing 1 to over 100 atoms.

Drug Discovery Text Generation

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

4 code implementations ICLR 2020 Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, Jian Tang

There are also some recent methods based on language models (e. g. graph2vec) but they tend to only consider certain substructures (e. g. subtrees) as graph representatives.

Graph Classification Molecular Property Prediction +2

vGraph: A Generative Model for Joint Community Detection and Node Representation Learning

1 code implementation NeurIPS 2019 Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-wei Huang, Jian Tang

Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks.

Community Detection Representation Learning +1

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