6 citations · 7 across the 3 of their papers we have counts for
5 papers
Automatic Machine Learning Derived from Scholarly Big Data
Asnat Greenstein-Messica, Roman Vainshtein, Gilad Katz +2
One of the challenging aspects of applying machine learning is the need to identify the algorithms that will perform best for a given dataset. This process can be difficult, time c…
Sequence Preserving Network Traffic Generation
Sigal Shaked, Amos Zamir, Roman Vainshtein +4
We present the Network Traffic Generator (NTG), a framework for perturbing recorded network traffic with the purpose of generating diverse but realistic background traffic for netw…
RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines
Doron Laadan, Roman Vainshtein, Yarden Curiel +2
The explosion of digital data has created multiple opportunities for organizations and individuals to leverage machine learning (ML) to transform the way they operate. However, the…
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering
Yuval Heffetz, Roman Vainstein, Gilad Katz +1
Automatic machine learning (AutoML) is an area of research aimed at automating machine learning (ML) activities that currently require human experts. One of the most challenging ta…
Assessing the Quality of Scientific Papers
Roman Vainshtein, Gilad Katz, Bracha Shapira +1
A multitude of factors are responsible for the overall quality of scientific papers, including readability, linguistic quality, fluency,semantic complexity, and of course domain-sp…