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**Click to Read Paper and Get Code**

* To appear in EACL 2017

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Language Generation with Recurrent Generative Adversarial Networks without Pre-training

Dec 21, 2017

Ofir Press, Amir Bar, Ben Bogin, Jonathan Berant, Lior Wolf

Dec 21, 2017

Ofir Press, Amir Bar, Ben Bogin, Jonathan Berant, Lior Wolf

* Presented at the 1st Workshop on Learning to Generate Natural Language at ICML 2017

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Fast and Accurate Reconstruction of Compressed Color Light Field

Mar 28, 2018

Ofir Nabati, David Mendlovic, Raja Giryes

Mar 28, 2018

Ofir Nabati, David Mendlovic, Raja Giryes

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On statistical learning via the lens of compression

Dec 30, 2016

Ofir David, Shay Moran, Amir Yehudayoff

This work continues the study of the relationship between sample compression schemes and statistical learning, which has been mostly investigated within the framework of binary classification. The central theme of this work is establishing equivalences between learnability and compressibility, and utilizing these equivalences in the study of statistical learning theory. We begin with the setting of multiclass categorization (zero/one loss). We prove that in this case learnability is equivalent to compression of logarithmic sample size, and that uniform convergence implies compression of constant size. We then consider Vapnik's general learning setting: we show that in order to extend the compressibility-learnability equivalence to this case, it is necessary to consider an approximate variant of compression. Finally, we provide some applications of the compressibility-learnability equivalences: (i) Agnostic-case learnability and realizable-case learnability are equivalent in multiclass categorization problems (in terms of sample complexity). (ii) This equivalence between agnostic-case learnability and realizable-case learnability does not hold for general learning problems: There exists a learning problem whose loss function takes just three values, under which agnostic-case and realizable-case learnability are not equivalent. (iii) Uniform convergence implies compression of constant size in multiclass categorization problems. Part of the argument includes an analysis of the uniform convergence rate in terms of the graph dimension, in which we improve upon previous bounds. (iv) A dichotomy for sample compression in multiclass categorization problems: If a non-trivial compression exists then a compression of logarithmic size exists. (v) A compactness theorem for multiclass categorization problems.
Dec 30, 2016

Ofir David, Shay Moran, Amir Yehudayoff

* Appeared in NIPS '16 (oral)

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Clear Skies Ahead: Towards Real-Time Automatic Sky Replacement in Video

Mar 06, 2019

Tavi Halperin, Harel Cain, Ofir Bibi, Michael Werman

Mar 06, 2019

Tavi Halperin, Harel Cain, Ofir Bibi, Michael Werman

* Eurographics 2019. Supplementary video: https://youtu.be/1uZ46YzX-pI

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Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

Oct 02, 2018

Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine

Oct 02, 2018

Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine

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On Detection of Faint Edges in Noisy Images

Jun 22, 2017

Nati Ofir, Meirav Galun, Sharon Alpert, Achi Brandt, Boaz Nadler, Ronen Basri

Jun 22, 2017

Nati Ofir, Meirav Galun, Sharon Alpert, Achi Brandt, Boaz Nadler, Ronen Basri

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