40 citations · 68 across the 3 of their papers we have counts for
3 papers
cs.LG2019★ 11 cited
Neural network gradient-based learning of black-box function interfaces
Alon Jacovi, Guy Hadash, Einat Kermany +4
Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of e…
cs.IR2018★ 40 cited
Rank and Rate: Multi-task Learning for Recommender Systems
Guy Hadash, Oren Sar Shalom, Rita Osadchy
The two main tasks in the Recommender Systems domain are the ranking and rating prediction tasks. The rating prediction task aims at predicting to what extent a user would like any…
cs.LG2018★ 17 cited
Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications
Guy Hadash, Einat Kermany, Boaz Carmeli +3
Existing applications include a huge amount of knowledge that is out of reach for deep neural networks. This paper presents a novel approach for integrating calls to existing appli…