activity
20182020
most citedThe Cost of Training NLP Models: A Concise Overview

114 citations · 115 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CL20201 cited

Technical Report: Auxiliary Tuning and its Application to Conditional Text Generation

Yoel Zeldes, Dan Padnos, Or Sharir +1

We introduce a simple and efficient method, called Auxiliary Tuning, for adapting a pre-trained Language Model to a novel task; we demonstrate this approach on the task of conditio…

cs.LG2020

The Depth-to-Width Interplay in Self-Attention

Yoav Levine, Noam Wies, Or Sharir +2

Self-attention architectures, which are rapidly pushing the frontier in natural language processing, demonstrate a surprising depth-inefficient behavior: previous works indicate th…

cs.CL2020114 cited

The Cost of Training NLP Models: A Concise Overview

Or Sharir, Barak Peleg, Yoav Shoham

We review the cost of training large-scale language models, and the drivers of these costs. The intended audience includes engineers and scientists budgeting their model-training e…

cs.CL2019

SenseBERT: Driving Some Sense into BERT

Yoav Levine, Barak Lenz, Or Dagan +6

The ability to learn from large unlabeled corpora has allowed neural language models to advance the frontier in natural language understanding. However, existing self-supervision t…

cond-mat.dis-nn2019

Deep autoregressive models for the efficient variational simulation of many-body quantum systems

Or Sharir, Yoav Levine, Noam Wies +2

Artificial Neural Networks were recently shown to be an efficient representation of highly-entangled many-body quantum states. In practical applications, neural-network states inhe…

quant-ph2018

Quantum Entanglement in Deep Learning Architectures

Yoav Levine, Or Sharir, Nadav Cohen +1

Modern deep learning has enabled unprecedented achievements in various domains. Nonetheless, employment of machine learning for wave function representations is focused on more tra…