Machine Learning

We work on algorithms for finding hidden structure in large data sets, using a combination of probabilistic modeling and deep learning, ranging from social media understanding, text mining, and consumer analytics to visual computing and content generation.

Latest Publications

Neural Sequential Phrase Grounding (SeqGROUND)

June 16, 2019
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019

Pelin Dogan (Disney Research/ETH Joint PhD) Leonid Sigal (University of British Columbia/Vector Institute) Markus Gross (Disney Research/ETH Zurich)

Disentangled Dynamic Representations from Unordered Data

December 2, 2018
Symposium on Advances in Approximate Bayesian Inference 2018

Leonhard Helminger (Disney Research/ETH Joint PhD) Aziz Djelouah (Disney Research) Markus Gross (Disney Research/ETH Zurich) Romann Weber (Disney Research)

Denoising Deep Monte Carlo Renderings

August 27, 2018
EU Computer Graphics Forum (EU CGF) 2018

Delio Vicini (Disney Research/Walt Disney Animation Studios) David Adler (Walt Disney Animation Studios) Jan Novak (Disney Research) Fabrice Rousselle (Disney Research) Brent Burley (Walt Disney Animation Studios)

Denoising with Kernel Prediction and Asymmetric Loss Functions

July 30, 2018

Thijs Vogels (Disney Research) Fabrice Rousselle (Disney Research) Brian McWilliams (Disney Research) Gerhard Röthlin (Disney Research) Alex Harvill (Pixar Animation Studios) David Adler (Walt Disney Animation Studios) Mark Meyer (Pixar Animation Studios) Jan Novak (Disney Research)