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
Differentiable Surface Splatting for Point-based Geometry Processing
November 1, 2019
ACM SIGGRAPH Asia 2019
Yifan Wang (ETH Zurich) Serena Felice (ETH Zurich) Wu Shihao (ETH Zurich) Cengiz Oztireli (DisneyResearch|Studios) Olga Sorkine-Hornung (ETH Zurich)
Spectrogram Feature Losses for Music Source Separation
September 2, 2019
Eusipco 2019
Abhimanyu Sahai (Disney Research/ETH Joint M.Sc.) Romann Weber (Disney Research) Brian McWilliams (Disney Research)
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)
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation
June 10, 2019
International Conference on Machine Learning (ICML) 2019
Marco Ancona (ETH Zurich) Cengiz Oztireli (Disney Research) 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)
PICA: Proactive Intelligent Conversational Agent for Interactive Narratives
November 5, 2018
International Conference on Intelligent Virtual Agents (IVA) 2018
Jessica Falk (ETH Zurich) Steven Poulakos (Disney Research) Mubbasir Kapadia (Rutgers University) Robert W. Sumner (Disney Research)