Brian McWilliams
Brian McWilliams
Research Scientist, DeepMind
Verified email at - Homepage
Cited by
Cited by
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
F Perazzi, J Pont-Tuset, B McWilliams, L Van Gool, M Gross, ...
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
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
A Fully Progressive Approach to Single-Image Super-Resolution
Y Wang, F Perazzi, B McWilliams, A Sorkine-Hornung, ...
arXiv preprint arXiv:1804.02900, 2018
Neural importance sampling
T Müller, B McWilliams, F Rousselle, M Gross, J Novák
ACM Transactions on Graphics (TOG) 38 (5), 145, 2019
Variance reduced stochastic gradient descent with neighbors
T Hofmann, A Lucchi, S Lacoste-Julien, B McWilliams
arXiv preprint arXiv:1506.03662, 2015
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
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
Subspace clustering of high-dimensional data: a predictive approach
B McWilliams, G Montana
Data Mining and Knowledge Discovery 28 (3), 736-772, 2014
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
Fast and robust least squares estimation in corrupted linear models
B McWilliams, G Krummenacher, M Lucic, JM Buhmann
Advances in Neural Information Processing Systems, 415-423, 2014
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
Correlated random features for fast semi-supervised learning
B McWilliams, D Balduzzi, JM Buhmann
arXiv preprint arXiv:1306.5554, 2013
Dual-loco: Distributing statistical estimation using random projections
C Heinze, B McWilliams, N Meinshausen
Artificial Intelligence and Statistics, 875-883, 2016
A variance reduced stochastic newton method
A Lucchi, B McWilliams, T Hofmann
arXiv preprint arXiv:1503.08316, 2015
LOCO: Distributing ridge regression with random projections
C Heinze, B McWilliams, N Meinshausen, G Krummenacher
arXiv:1406.3469, 2014
Representation Learning via Invariant Causal Mechanisms
J Mitrovic, B McWilliams, J Walker, L Buesing, C Blundell
arXiv preprint arXiv:2010.07922, 2020
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
Kernel-predicting convolutional neural networks for denoising
T Vogels, J Novák, F Rousselle, B McWilliams
US Patent 10,475,165, 2019
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
D Balduzzi, B McWilliams, T Butler-Yeoman, 2016
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