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Brian McWilliams
Brian McWilliams
Google DeepMind
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Title
Cited by
Cited by
Year
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
F Perazzi, J Pont-Tuset, B McWilliams, L Van Gool, M Gross, ...
19492016
The Shattered Gradients Problem: If resnets are the answer, then what is the question?
D Balduzzi, M Frean, L Leary, JP Lewis, KWD Ma, B McWilliams
arXiv preprint arXiv:1702.08591, 2017
4372017
Kernel-Predicting Convolutional Networks for Denoising Monte Carlo Renderings
S Bako, T Vogels, B McWilliams, M Meyer, J Novak, A Harvill, P Sen, ...
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2017) 36 (4), 2017
3132017
A Fully Progressive Approach to Single-Image Super-Resolution
Y Wang, F Perazzi, B McWilliams, A Sorkine-Hornung, ...
arXiv preprint arXiv:1804.02900, 2018
3072018
Neural importance sampling
T Müller, B McWilliams, F Rousselle, M Gross, J Novák
ACM Transactions on Graphics (TOG) 38 (5), 145, 2019
3062019
Representation learning via invariant causal mechanisms
J Mitrovic, B McWilliams, J Walker, L Buesing, C Blundell
arXiv preprint arXiv:2010.07922, 2020
2132020
Phasenet for video frame interpolation
S Meyer, A Djelouah, B McWilliams, A Sorkine-Hornung, M Gross, ...
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
1942018
Denoising with Kernel Prediction and Asymmetric Loss Functions
T Vogels, F Rousselle, B McWilliams, G Röthlin, A Harvill, D Adler, ...
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2018), 2018
1752018
Variance reduced stochastic gradient descent with neighbors
T Hofmann, A Lucchi, S Lacoste-Julien, B McWilliams
Advances in Neural Information Processing Systems 28, 2015
1742015
Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
S Kallweit, T Muller, B McWilliams, M Gross, J Novak
arXiv preprint arXiv:1709.05418, 2017
972017
Subspace clustering of high-dimensional data: a predictive approach
B McWilliams, G Montana
Data Mining and Knowledge Discovery 28, 736-772, 2014
852014
Social diversity and social preferences in mixed-motive reinforcement learning
KR McKee, I Gemp, B McWilliams, EA Duéńez-Guzmán, E Hughes, ...
arXiv preprint arXiv:2002.02325, 2020
842020
Learning outlier ensembles: The best of both worlds–supervised and unsupervised
B Micenková, B McWilliams, I Assent
Proceedings of the ACM SIGKDD 2014 Workshop on Outlier Detection and …, 2014
772014
Pushing the limits of self-supervised resnets: Can we outperform supervised learning without labels on imagenet?
N Tomasev, I Bica, B McWilliams, L Buesing, R Pascanu, C Blundell, ...
arXiv preprint arXiv:2201.05119, 2022
702022
Denoising Monte Carlo renderings using machine learning with importance sampling
T Vogels, F Rousselle, B McWilliams, M Meyer, J Novak
US Patent 10,572,979, 2020
652020
Fast and robust least squares estimation in corrupted linear models
B McWilliams, G Krummenacher, M Lucic, JM Buhmann
Advances in Neural Information Processing Systems 27, 2014
592014
TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter
X Zhang, Y Malkov, O Florez, S Park, B McWilliams, J Han, A El-Kishky
arXiv preprint arXiv:2209.07562, 2022
562022
Correlated random features for fast semi-supervised learning
B McWilliams, D Balduzzi, JM Buhmann
Advances in Neural Information Processing Systems 26, 2013
542013
Eigengame: PCA as a nash equilibrium
I Gemp, B McWilliams, C Vernade, T Graepel
arXiv preprint arXiv:2010.00554, 2020
502020
A variance reduced stochastic Newton method
A Lucchi, B McWilliams, T Hofmann
arXiv preprint arXiv:1503.08316, 2015
492015
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