activity
20172023
most citedGrad-SAM: Explaining Transformers via Gradient Self-Attention Maps

43 citations · 48 across the 10 of their papers we have counts for

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5 papers · 1 filter

cs.LG2023

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…

cs.LG2022★ 43 cited

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…

cs.LG2020★ 1 cited

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…

cs.LG2020

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…

cs.LG2019

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…