Or Sharir

About Me

I am a PhD student at the The Rachel and Selim Benin School of Computer Science and Engineering at the Hebrew University of Jerusalem. My advisor is Prof. Amnon Shashua. My main research interests are Neural Networks, Graphical Models and Machine Learning in general. I have recieved my B.Sc. degree in Physics, Mathematics and Computer Science from the Hebrew University. Click here for my complete CV.

If you are looking for my class notes, you can find them here (website and notes are in Hebrew).

Contact Information

Email:

or.sharir cs.huji.ac.il

Office:

Room B503, Rothberg Buildings, Givat Ram Campus.

News

• We released a paper developing specialized deep autoregressive models for the efficient simulation of quantum systems.—⁠February 11th, 2019.
• Our paper analysing the quantum entanglment supported by deep learning architectures was accepted to the prestige journal, Physical Review Letters.—⁠January 4th, 2019.
• Two of our papers, “On the Expressive Power of Overlapping Architectures of Deep Learning” and “Benefits of Depth for Long-Term Memory of Recurrent Networks”, were accepted for presentation at ICLR 2018, the former on the conference track and the latter on the workshop track.—⁠January 30th, 2018.
• Our paper “Sum-Product-Quotient Networks” was accepted to AISTATS 2018.—⁠December 22nd, 2017.
• We released a paper theoretically analyzing the benefits of depth to deep RNNs.—⁠October 25th, 2017.
• We released a paper proposing an extension to Sum-Product Networks that exponentially boosts their expressive power.—⁠October 12th, 2017.
• We released a paper on merging our tensorial analysis of ConvNets with Sum-Product Networks.—⁠October 13th, 2016.
• Our paper “On the Expressive Power of Deep Learning: A Tensor Analysis” was accepted to COLT 2016.—⁠April 26th, 2016.
• Our paper “Deep Simnets” was accepted to CVPR 2016.—⁠Feburary 29th, 2016.

Software

• PyKet — A Python framework for variational Monte-Carlo simulations of many-body quantum systems on top of Tensorflow.
• Github-MathJax (Chrome Extension) — a chrome extension for rendering LaTeX equations in Github repositories. Very useful for documenting research projects.
• reprochart — A usefuly python script for easily reproducible charts.
• Generative ConvACs (Experiments) — scripts for reproducing our experiments on Generative ConvACs.
• SimNets (Caffe Fork) — our fork of Caffe with the original implementation of the SimNets architecture.

Publications

• SenseBERT: Driving Some Sense into BERT. Arxiv preprint.
  Yoav Levine, Barak Lenz, Or Dagan, Dan Padnos, Or Sharir, Shai Shalev-Shwartz, Amnon Shashua, Yoav Shoham.
  Originally released on arXiv on August 15th, 2019.
• Deep Autoregressive Models for the Efficient Variational Simulation of Many-body Quantum Systems. Arxiv preprint.
  Or Sharir, Yoav Levine, Noam Wies, Giuseppe Carleo, and Amnon Shashua.
  Originally released on arXiv on Feburary 11th, 2019.
• Quantum Entanglement in Deep Learning Architectures. Physical Review Letters.
  Yoav Levine, Or Sharir, Nadav Cohen, and Amnon Shashua.
  Originally released on arXiv on March 26th, 2018.
• Benefits of Depth for Long-Term Memory of Recurrent Networks. ICLR 2018 Workshop.
  Yoav Levine, Or Sharir, and Amnon Shashua.
  Originally released on arXiv on October 25th, 2017.
• Sum-Product-Quotient Networks. AISTATS 2018.
  Or Sharir and Amnon Shashua.
  Originally released on arXiv on October 12th, 2017.
• On the Expressive Power of Overlapping Architectures of Deep Learning. ICLR 2018.
  Or Sharir and Amnon Shashua.
  Originally released on arXiv on March 6th, 2017.
• Tensorial Mixture Models. Preprint. (Source Code)
  Or Sharir, Ronen Tamari, Nadav Cohen, and Amnon Shashua.
  Originally released on arXiv on October 13th, 2016.
• On the Expressive Power of Deep Learning: A Tensor Analysis. COLT 2016.
  Nadav Cohen, Or Sharir, and Amnon Shashua.
  Originally released on arXiv on September 16th, 2015.
• Deep SimNets. CVPR 2016. (Source Code)
  Nadav Cohen, Or Sharir, and Amnon Shashua.
  Originally released on arXiv on June 9th, 2015.