Ethan Fetaya

I'm a postdoctoral fellow in the machine learning group at the University of Toronto, working with Rich Zemel and Max Welling. I am interested in deep learning, reasoning using knowledge graphs and modeling complex dynamical behaviour.

Previously, I did my PhD at the Weizmann Inst. under the supervision of Shimon Ullman in computer vision.

I did my M.Sc in mathematics at the Hebrew University under the supervision of Alex Lubotzky on Homological Error Correcting Codes and Systolic Geometry.

Email: ethanf [AT] cs [DOT] toronto [DOT] edu

Publications


Human Pose Estimation using Deep Consensus Voting.

European Conference on Computer Vision (ECCV), 2016.

Ita Lifshitz*, Ethan Fetaya*, Shimon Ullman. [Paper, Arxiv, Code , Poster ]
*equal contribution


Atoms of recognition in human and computer vision.

Proceedings of the National Academy of Sciences (PNAS), 2016.

Shimon Ullman, Liav Assif, Ethan Fetaya, and Daniel Harari. [Paper]


Unsupervised Ensemble Learning with Dependent Classifiers.

International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.

Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, and Yuval Kluger.[Paper, Supplementary]


StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation.

British Machine Vision Conference (BMVC), 2015.

Dan Levi, Noa Garnett and Ethan Fetaya. [Paper]


Learning Local Invariant Mahalanobis Distances.

International Conference on Machine Learning (ICML), 2015.

Ethan Fetaya and Shimon Ullman. [Paper]


Graph Approximation and Clustering on a Budget.

International Conference on Artificial Intelligence and Statistics (AISTATS), 2015.

Ethan Fetaya, Ohad Shamir and Shimon Ullman. [Paper, Supplementary ]


Bounding the distance of quantum surface codes.

Journal of Mathematical Physics, 2012.

Ethan Fetaya. [Paper]


Homological Error Correcting Codes and Systolic Geometry.

M.Sc Thesis.

Ethan Fetaya. [Arxiv]


Teaching

  • CSC411: Machine Learning and Data Mining, University of Toronto, Fall 2017

  • Introduction to Statistical Learning Theory, Weizmann Institute, Spring 2016

  • Introduction to Statistical Learning Theory, Weizmann Institute, Spring 2015