124 citations · 209 across the 31 of their papers we have counts for
37 papers
ConEx: Human-Interpretable Saliency Maps via Concept-Aware Attribution
Yehonatan Elisha, Oren Barkan, Ziv Weiss Haddad +1
Many visual explanation methods in computer vision highlight pixel importance but struggle to link these low-level cues to semantically meaningful concepts, limiting their interpre…
Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals
Meysam Varasteh, Veronika Bogina, Noam Koenigstein +1
The topic of explanation in recommender systems has seen steady research attention since the earliest days of the field. With some exceptions, this work has focused on the explanat…
Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness
Yehonatan Elisha, Oren Barkan, Noam Koenigstein
Vision Transformers (ViTs) often degrade under distribution shifts because they rely on spurious correlations, such as background cues, rather than semantically meaningful features…
Fidelity-Aware Recommendation Explanations via Stochastic Path Integration
Oren Barkan, Yahlly Schein, Yehonatan Elisha +3
Explanation fidelity, which measures how accurately an explanation reflects a model's true reasoning, remains critically underexplored in recommender systems. We introduce SPINRec…
Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems
Dor Arviv, Yehonatan Elisha, Oren Barkan +1
We present a method for extracting \emph{monosemantic} neurons, defined as latent dimensions that align with coherent and interpretable concepts, from user and item embeddings in r…
Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations
Yehonatan Elisha, Seffi Cohen, Oren Barkan +1
Saliency maps are widely used for visual explanations in deep learning, but a fundamental lack of consensus persists regarding their intended purpose and alignment with diverse use…
BEE: Metric-Adapted Explanations via Baseline Exploration-Exploitation
Oren Barkan, Yehonatan Elisha, Jonathan Weill +1
Two prominent challenges in explainability research involve 1) the nuanced evaluation of explanations and 2) the modeling of missing information through baseline representations. T…
InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers
Yakir Yehuda, Itzik Malkiel, Oren Barkan +3
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 t…
DiffMoog: a Differentiable Modular Synthesizer for Sound Matching
Noy Uzrad, Oren Barkan, Almog Elharar +4
This paper presents DiffMoog - a differentiable modular synthesizer with a comprehensive set of modules typically found in commercial instruments. Being differentiable, it allows i…
Visual Explanations via Iterated Integrated Attributions
Oren Barkan, Yehonatan Elisha, Yuval Asher +2
We introduce Iterated Integrated Attributions (IIA) - a generic method for explaining the predictions of vision models. IIA employs iterative integration across the input image, th…
Deep Integrated Explanations
Oren Barkan, Yehonatan Elisha, Jonathan Weill +3
This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the interme…
Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models
Oren Barkan, Yuval Asher, Amit Eshel +2
We present Learning to Explain (LTX), a model-agnostic framework designed for providing post-hoc explanations for vision models. The LTX framework introduces an "explainer" model t…
Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond
Oren Barkan, Tal Reiss, Jonathan Weill +4
Visual similarities discovery (VSD) is an important task with broad e-commerce applications. Given an image of a certain object, the goal of VSD is to retrieve images of different…
Representation Learning via Variational Bayesian Networks
Oren Barkan, Avi Caciularu, Idan Rejwan +4
We present Variational Bayesian Network (VBN) - a novel Bayesian entity representation learning model that utilizes hierarchical and relational side information and is particularly…
GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models
Itzik Malkiel, Uri Alon, Yakir Yehuda +4
Transcriptions of phone calls are of significant value across diverse fields, such as sales, customer service, healthcare, and law enforcement. Nevertheless, the analysis of these…
Detecting Security Patches via Behavioral Data in Code Repositories
Nitzan Farhi, Noam Koenigstein, Yuval Shavitt
The absolute majority of software today is developed collaboratively using collaborative version control tools such as Git. It is a common practice that once a vulnerability is det…
Interpreting BERT-based Text Similarity via Activation and Saliency Maps
Itzik Malkiel, Dvir Ginzburg, Oren Barkan +3
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. De…
MetricBERT: Text Representation Learning via Self-Supervised Triplet Training
Itzik Malkiel, Dvir Ginzburg, Oren Barkan +3
We present MetricBERT, a BERT-based model that learns to embed text under a well-defined similarity metric while simultaneously adhering to the ``traditional'' masked-language task…
Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps
Oren Barkan, Edan Hauon, Avi Caciularu +4
Transformer-based language models significantly advanced the state-of-the-art in many linguistic tasks. As this revolution continues, the ability to explain model predictions has b…
Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates
Oren Barkan, Roy Hirsch, Ori Katz +3
A major challenge in collaborative filtering methods is how to produce recommendations for cold items (items with no ratings), or integrate cold item into an existing catalog. Over…
GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps
Oren Barkan, Omri Armstrong, Amir Hertz +4
We present Gradient Activation Maps (GAM) - a machinery for explaining predictions made by visual similarity and classification models. By gleaning localized gradient and activatio…
Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference
Dvir Ginzburg, Itzik Malkiel, Oren Barkan +2
We present a novel model for the problem of ranking a collection of documents according to their semantic similarity to a source (query) document. While the problem of document-to-…
Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks
Oren Barkan, Jonathan Benchimol, Itamar Caspi +3
We present a hierarchical architecture based on Recurrent Neural Networks (RNNs) for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majo…
Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering
Oren Barkan, Yonatan Fuchs, Avi Caciularu +1
Two main challenges in recommender systems are modeling users with heterogeneous taste, and providing explainable recommendations. In this paper, we propose the neural Attentive Mu…