2 citations · 2 across the 7 of their papers we have counts for
3 papers · 1 filter
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…
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…
Gaussian Process Regression for Out-of-Sample Extension
Oren Barkan, Jonathan Weill, Amir Averbuch
Manifold learning methods are useful for high dimensional data analysis. Many of the existing methods produce a low dimensional representation that attempts to describe the intrins…