43 citations · 48 across the 10 of their papers we have counts for
13 papers
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…
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…
Discovery of Single Independent Latent Variable
Uri Shaham, Jonathan Svirsky, Ori Katz +1
Latent variable discovery is a central problem in data analysis with a broad range of applications in applied science. In this work, we consider data given as an invertible mixture…
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…
RecoBERT: A Catalog Language Model for Text-Based Recommendations
Itzik Malkiel, Oren Barkan, Avi Caciularu +3
Language models that utilize extensive self-supervised pre-training from unlabeled text, have recently shown to significantly advance the state-of-the-art performance in a variety…
Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing
Ori Katz, Roy R. Lederman, Ronen Talmon
In this paper, we consider data acquired by multimodal sensors capturing complementary aspects and features of a measured phenomenon. We focus on a scenario in which the measuremen…
Neural Attentive Multiview Machines
Oren Barkan, Ori Katz, Noam Koenigstein
An important problem in multiview representation learning is finding the optimal combination of views with respect to the specific task at hand. To this end, we introduce NAM: a Ne…
Attentive Item2Vec: Neural Attentive User Representations
Oren Barkan, Avi Caciularu, Ori Katz +1
Factorization methods for recommender systems tend to represent users as a single latent vector. However, user behavior and interests may change in the context of the recommendatio…
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding
Oren Barkan, Noam Razin, Itzik Malkiel +3
Recent state-of-the-art natural language understanding models, such as BERT and XLNet, score a pair of sentences (A and B) using multiple cross-attention operations - a process in…
InverSynth: Deep Estimation of Synthesizer Parameter Configurations from Audio Signals
Oren Barkan, David Tsiris, Ori Katz +1
Sound synthesis is a complex field that requires domain expertise. Manual tuning of synthesizer parameters to match a specific sound can be an exhaustive task, even for experienced…
Diffusion-based nonlinear filtering for multimodal data fusion with application to sleep stage assessment
Ori Katz, Ronen Talmon, Yu-Lun Lo +1
The problem of information fusion from multiple data-sets acquired by multimodal sensors has drawn significant research attention over the years. In this paper, we focus on a parti…