Search Results for author: Omri Ben-Eliezer

Found 6 papers, 0 papers with code

Active Learning Polynomial Threshold Functions

no code implementations24 Jan 2022 Omri Ben-Eliezer, Max Hopkins, Chutong Yang, Hantao Yu

We initiate the study of active learning polynomial threshold functions (PTFs).

Active Learning

What is Learned in Knowledge Graph Embeddings?

no code implementations19 Oct 2021 Michael R. Douglas, Michael Simkin, Omri Ben-Eliezer, Tianqi Wu, Peter Chin, Trung V. Dang, Andrew Wood

Their relative success is often credited in the literature to their ability to learn logical rules between the relations.

Knowledge Graph Embeddings

Learning Multimodal Affinities for Textual Editing in Images

no code implementations18 Mar 2021 Or Perel, Oron Anschel, Omri Ben-Eliezer, Shai Mazor, Hadar Averbuch-Elor

Nowadays, as cameras are rapidly adopted in our daily routine, images of documents are becoming both abundant and prevalent.

Adversarial Laws of Large Numbers and Optimal Regret in Online Classification

no code implementations22 Jan 2021 Noga Alon, Omri Ben-Eliezer, Yuval Dagan, Shay Moran, Moni Naor, Eylon Yogev

Laws of large numbers guarantee that given a large enough sample from some population, the measure of any fixed sub-population is well-estimated by its frequency in the sample.

General Classification online learning

READ: Recursive Autoencoders for Document Layout Generation

no code implementations1 Sep 2019 Akshay Gadi Patil, Omri Ben-Eliezer, Or Perel, Hadar Averbuch-Elor

Creating large varieties of plausible document layouts can be a tedious task, requiring numerous constraints to be satisfied, including local ones relating different semantic elements and global constraints on the general appearance and spacing.

Deleting and Testing Forbidden Patterns in Multi-Dimensional Arrays

no code implementations13 Jul 2016 Omri Ben-Eliezer, Simon Korman, Daniel Reichman

For any $\epsilon \in [0, 1]$ and any large enough pattern $P$ over any alphabet, other than a very small set of exceptional patterns, we design a tolerant tester that distinguishes between the case that the distance is at least $\epsilon$ and the case that it is at most $a_d \epsilon$, with query complexity and running time $c_d \epsilon^{-1}$, where $a_d < 1$ and $c_d$ depend only on $d$.

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