BrianMcWilliams
AI Research Scientist @ Google DeepMind
I am an AI research scientist at Google DeepMind, where I work on generative models for music and audio.
I have won several awards including, most recently, an Outstanding Paper Award at ICLR 2021. I am also passionate about the use of AI for conservation. See my Google Scholar profile and some representative papers below for more.
I've also held positions at Google Research, Twitter, and Disney Research where I led the Deep Learning group and worked on developing ML algorithms for production quality rendering, image and video processing, contributing to Toy Story 4 and Frozen 2.
Contact: bmcw@google.com
Generative Modelling
Transforming the future of music creation
"Music AI tools – a set of tools we’re designing with artists, songwriters, and producers to help bolster their creative processes."
MusicLM + MusicFX
"MusicLM generates high-fidelity music from text descriptions. Recent improvements include the integration of classifier-free guidance, improved acoustic tokens, and a new backbone architecture specifically designed to operate on such acoustic tokens, as well as applying SoundStorm, to achieve efficient high-fidelity audio generation."website | MusicFX | TechCrunch
MusicRL: Aligning Music Generation to Human Preferences
The first text-to-music model that incorporates human feedback at scale.G Cideron, S Girgin, M Verzetti, D Vincent, M Kastelic, Z Borsos, B McWilliams, V Ungureanu, O Bachem, O Pietquin, M Geist, L Hussenot, N Zeghidour, A Agostinelli | website | arXiv
Games and Multi-Agent.
Game theory as an engine for large-scale data analysis
"EigenGame maps out a new approach to solve fundamental ML problems."B McWilliams, I Gemp, C Vernade | DeepMind blog post
The generalized eigenvalue problem as a Nash equilibrium
We formulate the solution to the generalized eigenvalue problem as the Nash of a game, design a state-of-the-art algorithm to solve it, and solve problems 100x larger than before.I Gemp, C Chen, B McWilliams | ICLR 2023 notable paper (top 25% of accepted papers) | arXiv | code
⚃EigenGame Unloaded⚁ When playing games is better than optimizing
EigenGame's updates are biased if computed using minibatches of data, which hinders convergence and more sophisticated parallelism in the stochastic setting.We propose an unbiased stochastic update that is asymptotically equivalent to EigenGame, enjoys greater parallelism allowing computation on datasets of larger sample sizes, and outperforms EigenGame in experiments. We present applications to finding the principal components of massive datasets and performing spectral clustering of graphs. We analyze and discuss our proposed update in the context of EigenGame and the shift in perspective from optimization to games.I Gemp, B McWilliams, C Vernade, T Graepel | ICLR 2022 | arXiv | code
EigenGame: PCA as a Nash Equilibrium
We present a novel view on principal component analysis (PCA) as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their own utility function. We analyze the properties of this PCA game and the behavior of its gradient based updates. The resulting algorithm is naturally decentralized and hence parallelizable through message passing. We demonstrate the scalability of the algorithm with experiments on large image datasets and neural network activations. We discuss how this new view of PCA as a differentiable game can lead to further algorithmic developments and insights.I Gemp, B McWilliams, C Vernade, T Graepel | ICLR 2021 Outstanding Paper Award | video | arXiv | code
Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning
K McKee, E Hughes, J Leibo, I Gemp, B McWilliams, E Duéñez-Guzmán | AAMAS 2020 | arXiv
The Unreasonable Effectiveness of Adam on Cycles
I Gemp, B McWilliams | Bridging Game Theory & Deep Learning 2019 | paper
Representation Learning.
Using machine learning to accelerate ecological research
"DeepMind is collaborating with ecologists and conservationists to develop machine learning methods to help study the behavioural dynamics of an entire African animal community in the Serengeti National Park and Grumeti Reserve in Tanzania."S Petersen, et al | DeepMind blog post
Pushing the limits of self-supervised ResNets: can we outperform supervised learning without labels on ImageNet?
N Tomasev, I Bica, B McWilliams et al. | arXiv
Correlated random features for fast semi-supervised learning.
This paper presents a fast semi-supervised algorithm for regression and classification. The algorithm draws on two main ideas. First, it generates two views consisting of computationally inexpensive random features. Second, multiview regression, using Canonical Correlation Analysis on unlabeled data, biases the regression towards useful features.B McWilliams, D Balduzzi, J Buhmann | NeurIPS 2013 | arXiv
Subspace clustering of high-dimensional data: a predictive approach
B McWilliams, G Montana | Data Mining and Knowledge Discovery 2014 | arXiv
Multi-view predictive partitioning in high dimensions
Many modern data mining applications are concerned with the analysis of datasets in which the observations are described by paired high-dimensional vectorial representations or "views". Some typical examples can be found in web mining and genomics applications. In this article we present an algorithm for data clustering with multiple views which relies on a novel criterion of predictive similarity between data points.B McWilliams, G Montana | Statistical Analysis and Data Mining 2012 | arXiv
Graphics, Rendering and Vision.
Neural Importance Sampling
T Müller, B McWilliams, F Rousselle, M Gross, J Novak | SIGGRAPH 2019 | arXiv | video | interactive test suite
Denoising with Kernel Prediction and Asymmetric Loss Functions
T Vogels, F Rousselle, B McWilliams, G Rothlin, A Harvill, D Adler, M Meyer, J Novak | SIGGRAPH 2018 | paper | video | online test suite | slides
A Fully Progressive Approach to Single-Image Super-Resolution
Y Wang, F Perazzi, B McWilliams, A Sorkine-Hornung, O Sorkine-Hornung, C Schröers | CVPR NTIRE 2018 | arXiv | code | 2 minute summary
Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
S Kallweit, T Muller, B McWilliams, M Gross, J Novak | SIGGRAPH Asia 2017 | arXiv | video | project page | 2 minute summary
Kernel-predicting Convolutional Networks for Denoising Monte Carlo Renderings
S Bako, T Vogels, B McWilliams, M Meyer, J Novak, A Harvill, P Sen, T DeRose, F Rouselle | SIGGRAPH 2017 | Project page
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
F Perazzi, J Pont-Tuset, B McWilliams, M Gross, L Van Gool, A Sorkine-Hornung | CVPR 2016 | Project page
Deep Learning Theory, Optimization and Statistics
The Shattered Gradients Problem: If resnets are the answer, then what is the question?
D Balduzzi, M Frean, L Leary, JP Lewis, K Ma, B McWilliams | ICML 2017 | PADL best paper | arXiv | video
Preserving Differential Privacy Between Features in Distributed Estimation
C Heinze-Deml, B McWilliams, N Meinshausen | Stat | arXiv
Scalable Adaptive Stochastic Optimization Using Random Projections
G Krummenacher, B McWilliams, Y Kilcher, J Buhmann, N Meinshausen | NeurIPS 2016 | arXiv
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
C Heinze, B McWilliams, N Meinshausen | AISTATS 2016 Oral (top 6%) | arXiv | software
Variance Reduced Stochastic Gradient Descent with Neighbors
T Hofmann, A Lucchi, S Lacoste-Julien, B McWilliams | NeurIPS 2015 | arXiv
Fast and Robust Least Squares Estimation in Corrupted Linear Models
B McWilliams, G Krummenacher, M Lučić, J Buhmann | NeurIPS 2014 Spotlight (top 4%) | arXiv | slides | video