Search Results for author: Jonathan Weill

Found 8 papers, 3 papers with code

In Search of Truth: An Interrogation Approach to Hallucination Detection

1 code implementation5 Mar 2024 Yakir Yehuda, Itzik Malkiel, Oren Barkan, Jonathan Weill, Royi Ronen, Noam Koenigstein

Despite the many advances of Large Language Models (LLMs) and their unprecedented rapid evolution, their impact and integration into every facet of our daily lives is limited due to various reasons.


Deep Integrated Explanations

1 code implementation23 Oct 2023 Oren Barkan, Yehonatan Elisha, Jonathan Weill, Yuval Asher, Amit Eshel, Noam Koenigstein

This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models.

Representation Learning via Variational Bayesian Networks

no code implementations28 Jun 2023 Oren Barkan, Avi Caciularu, Idan Rejwan, Ori Katz, Jonathan Weill, Itzik Malkiel, Noam Koenigstein

We present Variational Bayesian Network (VBN) - a novel Bayesian entity representation learning model that utilizes hierarchical and relational side information and is particularly useful for modeling entities in the ``long-tail'', where the data is scarce.

Bayesian Inference Representation Learning

Interpreting BERT-based Text Similarity via Activation and Saliency Maps

no code implementations13 Aug 2022 Itzik Malkiel, Dvir Ginzburg, Oren Barkan, Avi Caciularu, Jonathan Weill, Noam Koenigstein

Recently, there has been growing interest in the ability of Transformer-based models to produce meaningful embeddings of text with several applications, such as text similarity.

text similarity

Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates

no code implementations12 Dec 2021 Oren Barkan, Roy Hirsch, Ori Katz, Avi Caciularu, Jonathan Weill, Noam Koenigstein

Next, we propose a novel hybrid recommendation algorithm that bridges these two conflicting objectives and enables a harmonized balance between preserving high accuracy for warm items while effectively promoting completely cold items.

Collaborative Filtering

Gaussian Process Regression for Out-of-Sample Extension

no code implementations7 Mar 2016 Oren Barkan, Jonathan Weill, Amir Averbuch

Many of the existing methods produce a low dimensional representation that attempts to describe the intrinsic geometric structure of the original data.


Adaptive Compressed Tomography Sensing

no code implementations CVPR 2013 Oren Barkan, Jonathan Weill, Amir Averbuch, Shai Dekel

One of the main challenges in Computed Tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the CT image.

Computed Tomography (CT)

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