Automatic Machine Learning Derived from Scholarly Big Data

no code implementations • 6 Mar 2020 • Asnat Greenstein-Messica, Roman Vainshtein, Gilad Katz, Bracha Shapira, Lior Rokach

One of the challenging aspects of applying machine learning is the need to identify the algorithms that will perform best for a given dataset.

BIG-bench Machine Learning

Sequence Preserving Network Traffic Generation

no code implementations • 23 Feb 2020 • Sigal Shaked, Amos Zamir, Roman Vainshtein, Moshe Unger, Lior Rokach, Rami Puzis, Bracha Shapira

We examined two methods for extracting sequences of activities: a Markov model and a neural language model.

Language Modelling Two-sample testing

RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines

no code implementations • 31 Oct 2019 • Doron Laadan, Roman Vainshtein, Yarden Curiel, Gilad Katz, Lior Rokach

In this study, we propose RankML, a meta-learning based approach for predicting the performance of whole machine learning pipelines.

BIG-bench Machine Learning Meta-Learning

Assessing the Quality of Scientific Papers

no code implementations • 12 Aug 2019 • Roman Vainshtein, Gilad Katz, Bracha Shapira, Lior Rokach

In this paper, we propose a measure and method for assessing the overall quality of the scientific papers in a particular field of study.