papers

Publications (29)

cs.MA2014

Simulation leagues: Analysis of competition formats

David Budden, Peter Wang, Oliver Obst +1

cs.LG2019

TF-Replicator: Distributed Machine Learning for Researchers

Peter Buchlovsky, David Budden, Dominik Grewe +9

cs.AI2018

DeepMind Control Suite

Yuval Tassa, Yotam Doron, Alistair Muldal +9

cs.CV2017

Toward Streaming Synapse Detection with Compositional ConvNets

Shibani Santurkar, David Budden, Alexander Matveev +4

cs.RO2020

Scaling data-driven robotics with reward sketching and batch reinforcement learning

Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov +13

cs.LG2018

Distributed Distributional Deterministic Policy Gradients

Gabriel Barth-Maron, Matthew W. Hoffman, David Budden +6

cs.LG2018

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL

Tom Le Paine, Sergio Gómez Colmenarejo, Ziyu Wang +8

cs.LG2022

The CLRS Algorithmic Reasoning Benchmark

Petar Veličković, Adrià Puigdomènech Badia, David Budden +5

cs.LG2020

A Combinatorial Perspective on Transfer Learning

Jianan Wang, Eren Sezener, David Budden +2

cs.AI2019

A Generalized Framework for Population Based Training

Ang Li, Aleksandra Spyra, Sagi Perel +6

cs.CL2022

Unified Scaling Laws for Routed Language Models

Aidan Clark, Diego de las Casas, Aurelia Guy +23

cs.RO2013

RANSAC: Identification of Higher-Order Geometric Features and Applications in Humanoid Robot Soccer

Madison Flannery, Shannon Fenn, David Budden

cs.LG2018

Playing hard exploration games by watching YouTube

Yusuf Aytar, Tobias Pfaff, David Budden +3

cs.LG2020

Task-Relevant Adversarial Imitation Learning

Konrad Zolna, Scott Reed, Alexander Novikov +6

cs.LG2024

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

Aleksandar Botev, Soham De, Samuel L Smith +59

cs.LG2020

Gaussian Gated Linear Networks

David Budden, Adam Marblestone, Eren Sezener +3

cs.LG2019

Sample Efficient Adaptive Text-to-Speech

Yutian Chen, Yannis Assael, Brendan Shillingford +11

cs.CV2017

Generative Compression

Shibani Santurkar, David Budden, Nir Shavit

cs.LG2024

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Soham De, Samuel L. Smith, Anushan Fernando +14

cs.CL2022

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

Jack W. Rae, Sebastian Borgeaud, Trevor Cai +77

cs.LG2018

Distributed Prioritized Experience Replay

Dan Horgan, John Quan, David Budden +4

cs.LG2018

Observe and Look Further: Achieving Consistent Performance on Atari

Tobias Pohlen, Bilal Piot, Todd Hester +10

cs.CV2014

Addressing the non-functional requirements of computer vision systems: A case study

Shannon Fenn, Alexandre Mendes, David Budden

cs.LG2020

Online Learning in Contextual Bandits using Gated Linear Networks

Eren Sezener, Marcus Hutter, David Budden +2

cs.CV2017

Deep Tensor Convolution on Multicores

David Budden, Alexander Matveev, Shibani Santurkar +2

cs.LG2020

Modular Meta-Learning with Shrinkage

Yutian Chen, Abram L. Friesen, Feryal Behbahani +4

cs.LG2020

Gated Linear Networks

Joel Veness, Tor Lattimore, David Budden +8

cs.LG2021

Large-scale graph representation learning with very deep GNNs and self-supervision

Ravichandra Addanki, Peter W. Battaglia, David Budden +8

q-bio.QM2016

A Multi-Pass Approach to Large-Scale Connectomics

Yaron Meirovitch, Alexander Matveev, Hayk Saribekyan +8