Distributed Distributional Deterministic Policy Gradients. - Gabriel Barth-Maron; Matthew W. Hoffman; David Budden; Will Dabney; Dan Horgan; Dhruva TB; Alistair Muldal; Nicolas Heess; Timothy P. Lillicrap (ICLR (Poster), 2018)

Distributed Prioritized Experience Replay. - Dan Horgan; John Quan; David Budden; Gabriel Barth-Maron; Matteo Hessel; Hado van Hasselt; David Silver (ICLR (Poster), 2018)

Playing hard exploration games by watching YouTube. - Yusuf Aytar; Tobias Pfaff; David Budden; Tom Le Paine; Ziyu Wang 001; Nando de Freitas (NeurIPS, 2018)

DeepMind Control Suite. - Yuval Tassa; Yotam Doron; Alistair Muldal; Tom Erez; Yazhe Li; Diego de Las Casas; David Budden; Abbas Abdolmaleki; Josh Merel; Andrew Lefrancq; Timothy P. Lillicrap; Martin A. Riedmiller (CoRR, 2018)

Observe and Look Further: Achieving Consistent Performance on Atari. - Tobias Pohlen; Bilal Piot; Todd Hester; Mohammad Gheshlaghi Azar; Dan Horgan; David Budden; Gabriel Barth-Maron; Hado van Hasselt; John Quan; Mel Vecerík; Matteo Hessel; Rémi Munos; et al. (CoRR, 2018)

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL. - Tom Le Paine; Sergio Gomez Colmenarejo; Ziyu Wang 001; Scott E. Reed; Yusuf Aytar; Tobias Pfaff; Matthew W. Hoffman; Gabriel Barth-Maron; Serkan Cabi; David Budden; Nando de Freitas (CoRR, 2018)

Sample Efficient Adaptive Text-to-Speech. - Yutian Chen 001; Yannis M. Assael; Brendan Shillingford; David Budden; Scott E. Reed; Heiga Zen; Quan Wang; Luis C. Cobo; Andrew Trask; Ben Laurie; Çaglar Gülçehre; Aäron van den Oord; et al. (ICLR (Poster), 2019)

A Generalized Framework for Population Based Training. - Ang Li 001; Ola Spyra; Sagi Perel; Valentin Dalibard; Max Jaderberg; Chenjie Gu; David Budden; Tim Harley; Pramod Gupta (KDD, 2019)

TF-Replicator: Distributed Machine Learning for Researchers. - Peter Buchlovsky; David Budden; Dominik Grewe; Chris Jones; John Aslanides; Frederic Besse; Andy Brock; Aidan Clark; Sergio Gomez Colmenarejo; Aedan Pope; Fabio Viola; Dan Belov (CoRR, 2019)

A Framework for Data-Driven Robotics. - Serkan Cabi; Sergio Gómez Colmenarejo; Alexander Novikov 001; Ksenia Konyushkova; Scott E. Reed; Rae Jeong; Konrad Zolna; Yusuf Aytar; David Budden; Mel Vecerík; Oleg Sushkov; David Barker; et al. (CoRR, 2019)

Gated Linear Networks. - Joel Veness; Tor Lattimore; Avishkar Bhoopchand; David Budden; Christopher Mattern; Agnieszka Grabska-Barwinska; Peter Toth; Simon Schmitt; Marcus Hutter (CoRR, 2019)

Grandmaster level in StarCraft II using multi-agent reinforcement learning. - Oriol Vinyals; Igor Babuschkin; Wojciech M. Czarnecki; Michaël Mathieu; Andrew Dudzik; Junyoung Chung; David H. Choi; Richard Powell; Timo Ewalds; Petko Georgiev; Junhyuk Oh; Dan Horgan; et al. (Nat., 2019)

Task-Relevant Adversarial Imitation Learning. - Konrad Zolna; Scott E. Reed; Alexander Novikov 001; Sergio Gómez Colmenarejo; David Budden; Serkan Cabi; Misha Denil; Nando de Freitas; Ziyu Wang 001 (CoRL, 2020)

Gaussian Gated Linear Networks. - David Budden; Adam H. Marblestone; Eren Sezener; Tor Lattimore; Gregory Wayne; Joel Veness (NeurIPS, 2020)

Modular Meta-Learning with Shrinkage. - Yutian Chen 001; Abram L. Friesen; Feryal M. P. Behbahani; Arnaud Doucet; David Budden; Matthew Hoffman 002; Nando de Freitas (NeurIPS, 2020)

Online Learning in Contextual Bandits using Gated Linear Networks. - Eren Sezener; Marcus Hutter; David Budden; Jianan Wang; Joel Veness (NeurIPS, 2020)

A Combinatorial Perspective on Transfer Learning. - Jianan Wang; Eren Sezener; David Budden; Marcus Hutter; Joel Veness (NeurIPS, 2020)

Scaling data-driven robotics with reward sketching and batch reinforcement learning. - Serkan Cabi; Sergio Gómez Colmenarejo; Alexander Novikov 001; Ksenia Konyushkova; Scott E. Reed; Rae Jeong; Konrad Zolna; Yusuf Aytar; David Budden; Mel Vecerík; Oleg Sushkov; David Barker; et al. (Robotics: Science and Systems, 2020)

Gated Linear Networks. - Joel Veness; Tor Lattimore; David Budden; Avishkar Bhoopchand; Christopher Mattern; Agnieszka Grabska-Barwinska; Eren Sezener; Jianan Wang; Peter Toth; Simon Schmitt; Marcus Hutter (AAAI, 2021)

Large-scale graph representation learning with very deep GNNs and self-supervision. - Ravichandra Addanki; Peter W. Battaglia; David Budden; Andreea Deac; Jonathan Godwin; Thomas Keck; Wai Lok Sibon Li; Alvaro Sanchez-Gonzalez; Jacklynn Stott; Shantanu Thakoor; Petar Velickovic (CoRR, 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)

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)

The CLRS Algorithmic Reasoning Benchmark. - Petar Velickovic; Adrià Puigdomènech Badia; David Budden; Razvan Pascanu; Andrea Banino; Misha Dashevskiy; Raia Hadsell; Charles Blundell (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. (CoRR, 2022)

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models. - Soham De; Samuel L. Smith; Anushan Fernando; Aleksandar Botev; George-Cristian Muraru; Albert Gu; Ruba Haroun; Leonard Berrada; Yutian Chen 001; Srivatsan Srinivasan; Guillaume Desjardins; Arnaud Doucet; et al. (CoRR, 2024)

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. - Aleksandar Botev; Soham De; Samuel L. Smith; Anushan Fernando; George-Cristian Muraru; Ruba Haroun; Leonard Berrada; Razvan Pascanu; Pier Giuseppe Sessa; Robert Dadashi; Léonard Hussenot; Johan Ferret; et al. (CoRR, 2024)