Generalized Planning With Deep Reinforcement Learning

May 05, 2020
Or Rivlin, Tamir Hazan, Erez Karpas

* 13 pages 

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On the generalization of bayesian deep nets for multi-class classification

Feb 23, 2020
Yossi Adi, Yaniv Nemcovsky, Alex Schwing, Tamir Hazan


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A Formal Approach to Explainability

Jan 15, 2020
Lior Wolf, Tomer Galanti, Tamir Hazan

* Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, January 2019, Pages 255-261 

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Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces

Jun 14, 2019
Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow


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Factor Graph Attention

Apr 11, 2019
Idan Schwartz, Seunghak Yu, Tamir Hazan, Alexander Schwing

* Accepted to CVPR 2019 

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A Simple Baseline for Audio-Visual Scene-Aware Dialog

Apr 11, 2019
Idan Schwartz, Alexander Schwing, Tamir Hazan

* Accepted to CVPR 2019 

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Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder

Oct 11, 2018
Guy Lorberbom, Andreea Gane, Tommi Jaakkola, Tamir Hazan


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High-Order Attention Models for Visual Question Answering

Nov 12, 2017
Idan Schwartz, Alexander G. Schwing, Tamir Hazan

* 9 pages, 8 figures, NIPS 2017 

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High Dimensional Inference with Random Maximum A-Posteriori Perturbations

May 30, 2017
Tamir Hazan, Francesco Orabona, Anand D. Sarwate, Subhransu Maji, Tommi Jaakkola

* 47 pages, 10 figures, under review 

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Tight Bounds for Bandit Combinatorial Optimization

Feb 24, 2017
Alon Cohen, Tamir Hazan, Tomer Koren


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Co-segmentation for Space-Time Co-located Collections

Jan 31, 2017
Hadar Averbuch-Elor, Johannes Kopf, Tamir Hazan, Daniel Cohen-Or


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Online Learning with Feedback Graphs Without the Graphs

May 23, 2016
Alon Cohen, Tamir Hazan, Tomer Koren


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Multicuts and Perturb & MAP for Probabilistic Graph Clustering

Jan 09, 2016
Jörg Hendrik Kappes, Paul Swoboda, Bogdan Savchynskyy, Tamir Hazan, Christoph Schnörr


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Steps Toward Deep Kernel Methods from Infinite Neural Networks

Sep 02, 2015
Tamir Hazan, Tommi Jaakkola


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On Measure Concentration of Random Maximum A-Posteriori Perturbations

Oct 15, 2013
Francesco Orabona, Tamir Hazan, Anand D. Sarwate, Tommi Jaakkola


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On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations

Sep 29, 2013
Tamir Hazan, Subhransu Maji, Tommi Jaakkola


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Blending Learning and Inference in Structured Prediction

Aug 30, 2013
Tamir Hazan, Alexander Schwing, David McAllester, Raquel Urtasun


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Tightening Fractional Covering Upper Bounds on the Partition Function for High-Order Region Graphs

Oct 16, 2012
Tamir Hazan, Jian Peng, Amnon Shashua

* Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012) 

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Approximated Structured Prediction for Learning Large Scale Graphical Models

Jul 09, 2012
Tamir Hazan, Raquel Urtasun


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On the Partition Function and Random Maximum A-Posteriori Perturbations

Jun 27, 2012
Tamir Hazan, Tommi Jaakkola

* Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012) 

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Efficient Structured Prediction with Latent Variables for General Graphical Models

Jun 27, 2012
Alexander Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun

* Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012) 

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Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies

Jun 13, 2012
Tamir Hazan, Amnon Shashua

* Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008) 

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Continuous Markov Random Fields for Robust Stereo Estimation

Apr 06, 2012
Koichiro Yamaguchi, Tamir Hazan, David McAllester, Raquel Urtasun


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Norm-Product Belief Propagation: Primal-Dual Message-Passing for Approximate Inference

Jun 28, 2010
Tamir Hazan, Amnon Shashua


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