Search Results for author: Lior Rokach

Found 42 papers, 13 papers with code

A Universal Adversarial Policy for Text Classifiers

1 code implementation19 Jun 2022 Gallil Maimon, Lior Rokach

We achieve this by learning a single search policy over a predefined set of semantics preserving text alterations, on many texts.

Boosting Anomaly Detection Using Unsupervised Diverse Test-Time Augmentation

no code implementations29 Oct 2021 Seffi Cohen, Niv Goldshlager, Lior Rokach, Bracha Shapira

Anomaly detection is a well-known task that involves the identification of abnormal events that occur relatively infrequently.

Anomaly Detection

Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target Scenes

1 code implementation10 Feb 2021 Yael Mathov, Lior Rokach, Yuval Elovici

We use the framework to create a patch for an everyday scene and evaluate its performance using a novel evaluation process that ensures that our results are reproducible in both the digital space and the real world.

Inference Attack Object Reconstruction +1

Automatic selection of clustering algorithms using supervised graph embedding

1 code implementation16 Nov 2020 Noy Cohen-Shapira, Lior Rokach

The widespread adoption of machine learning (ML) techniques and the extensive expertise required to apply them have led to increased interest in automated ML solutions that reduce the need for human intervention.

AutoML Graph Embedding +1

Approximating Aggregated SQL Queries With LSTM Networks

no code implementations25 Oct 2020 Nir Regev, Lior Rokach, Asaf Shabtai

We use LSTM network to learn the relationship between queries and their results, and to provide a rapid inference layer for predicting query results.

Lessons Learned from Applying off-the-shelf BERT: There is no Silver Bullet

no code implementations15 Sep 2020 Victor Makarenkov, Lior Rokach

One of the challenges in the NLP field is training large classification models, a task that is both difficult and tedious.

Classification General Classification +1

Evolving Context-Aware Recommender Systems With Users in Mind

no code implementations30 Jul 2020 Amit Livne, Eliad Shem Tov, Adir Solomon, Achiya Elyasaf, Bracha Shapira, Lior Rokach

An empirical analysis of our results validates that our proposed approach outperforms SOTA CARS models while improving transparency and explainability to the user.

Recommendation Systems

A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach

no code implementations28 Jul 2020 Hagit Grushka-Cohen, Raphael Cohen, Bracha Shapira, Jacob Moran-Gilad, Lior Rokach

We find that individuals can be ranked for effective testing using a few simple features, and that ranking them using such models we can capture 65% (CI: 64. 7%-68. 3%) of the positive individuals using less than 20% of the testing capacity or 92. 1% (CI: 91. 1%-93. 2%) of positives individuals using 70% of the capacity, allowing reserving a significant portion of the tests for population studies.

Decision Making Multi-Armed Bandits

Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate

no code implementations26 Jul 2020 Amit Livne, Roy Dor, Eyal Mazuz, Tamar Didi, Bracha Shapira, Lior Rokach

Learning sophisticated models to understand and predict user behavior is essential for maximizing the CTR in recommendation systems.

Click-Through Rate Prediction Feature Engineering +1

Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

no code implementations5 Jul 2020 Ihai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach

In recent years machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security.

Adversarial Attack BIG-bench Machine Learning

PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction

1 code implementation9 Jun 2020 Eli Simhayev, Gilad Katz, Lior Rokach

Improving the robustness of neural nets in regression tasks is key to their application in multiple domains.

Prediction Intervals Value prediction

Automatic Machine Learning Derived from Scholarly Big Data

no code implementations6 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

PrivGen: Preserving Privacy of Sequences Through Data Generation

1 code implementation23 Feb 2020 Sigal Shaked, Lior Rokach

Since in many cases the researcher does not need the data as is and instead is only interested in the patterns that exist in the data, we propose PrivGen, an innovative method for generating data that maintains patterns and characteristics of the source data.

Sequence Preserving Network Traffic Generation

no code implementations23 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 implementations31 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

Diversifying Database Activity Monitoring with Bandits

no code implementations23 Oct 2019 Hagit Grushka-Cohen, Ofer Biller, Oded Sofer, Lior Rokach, Bracha Shapira

Database activity monitoring (DAM) systems are commonly used by organizations to protect the organizational data, knowledge and intellectual properties.

Event Detection Recommendation Systems

Deep Context-Aware Recommender System Utilizing Sequential Latent Context

no code implementations9 Sep 2019 Amit Livne, Moshe Unger, Bracha Shapira, Lior Rokach

Recent research has shown that modeling contextual information as a latent vector may address the sparsity and dimensionality challenges.

Collaborative Filtering Recommendation Systems

Assessing the Quality of Scientific Papers

no code implementations12 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.

New Item Consumption Prediction Using Deep Learning

no code implementations5 May 2019 Michael Shekasta, Gilad Katz, Asnat Greenstein-Messica, Lior Rokach, Bracha Shapira

Our experiments show that PISA outperforms a well-known deep learning baseline when new items are introduced.

Recommendation Systems

Online Budgeted Learning for Classifier Induction

no code implementations13 Mar 2019 Eran Fainman, Bracha Shapira, Lior Rokach, Yisroel Mirsky

In online learning, the challenge is to find the optimum set of features to be acquired from each instance upon arrival from a data stream.

online learning

Personal Dynamic Cost-Aware Sensing for Latent Context Detection

no code implementations13 Mar 2019 Saar Tal, Bracha Shapira, Lior Rokach

Results show that by applying a dynamic sampling policy, our method naturally balances information loss and energy consumption and outperforms the static approach.% We compared the performance of our method with another state of the art dynamic sampling method and demonstrate its consistent superiority in various measures.

Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures

1 code implementation11 Mar 2019 Guy Shtar, Lior Rokach, Bracha Shapira

Finally, we create an ensemble-based classifier using AMF, AMFP, and existing link prediction methods and obtain an area under the receiver operating characteristic curve of 0. 814 and 0. 991 for the retrospective and the holdout analyses.

Graph Similarity Link Prediction

Explaining Anomalies Detected by Autoencoders Using SHAP

2 code implementations6 Mar 2019 Liat Antwarg, Ronnie Mindlin Miller, Bracha Shapira, Lior Rokach

Anomaly detection algorithms are often thought to be limited because they don't facilitate the process of validating results performed by domain experts.

Anomaly Detection Outlier Detection

Defense Methods Against Adversarial Examples for Recurrent Neural Networks

no code implementations28 Jan 2019 Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach

Using our methods we were able to decrease the effectiveness of such attack from 99. 9% to 15%.

Cryptography and Security

Implicit Dimension Identification in User-Generated Text with LSTM Networks

no code implementations26 Jan 2019 Victor Makarenkov, Ido Guy, Niva Hazon, Tamar Meisels, Bracha Shapira, Lior Rokach

In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative.

Community Question Answering

Choosing the Right Word: Using Bidirectional LSTM Tagger for Writing Support Systems

1 code implementation8 Jan 2019 Victor Makarenkov, Lior Rokach, Bracha Shapira

We use a bidirectional Recurrent Neural Network (RNN) with LSTM for learning the proper word choice based on a word's sentential context.

Grammatical Error Correction Machine Translation +1

Wikibook-Bot - Automatic Generation of a Wikipedia Book

no code implementations28 Dec 2018 Shahar Admati, Lior Rokach, Bracha Shapira

A Wikipedia book (known as Wikibook) is a collection of Wikipedia articles on a particular theme that is organized as a book.

BIG-bench Machine Learning

Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers

no code implementations23 Apr 2018 Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach

In this paper, we present a generic, query-efficient black-box attack against API call-based machine learning malware classifiers.

Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

no code implementations19 Jul 2017 Ishai Rosenberg, Asaf Shabtai, Lior Rokach, Yuval Elovici

In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e. g., printable strings) that will be misclassified by the classifier without affecting the malware functionality.

BIG-bench Machine Learning

Language Models with Pre-Trained (GloVe) Word Embeddings

1 code implementation12 Oct 2016 Victor Makarenkov, Bracha Shapira, Lior Rokach

In this work we implement a training of a Language Model (LM), using Recurrent Neural Network (RNN) and GloVe word embeddings, introduced by Pennigton et al. in [1].

Language Modelling Word Embeddings

Wikiometrics: A Wikipedia Based Ranking System

no code implementations6 Jan 2016 Gilad Katz, Lior Rokach

We present a new concept - Wikiometrics - the derivation of metrics and indicators from Wikipedia.

Ensemble Methods for Multi-label Classification

no code implementations6 Jul 2013 Lior Rokach, Alon Schclar, Ehud Itach

In the RAndom k-labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels.

Classification General Classification +1

Ensembles of Classifiers based on Dimensionality Reduction

no code implementations19 May 2013 Alon Schclar, Lior Rokach, Amir Amit

These versions are obtained by applying dimensionality reduction to the original training set using different values of the input parameters.

Dimensionality Reduction

Transfer Learning for Content-Based Recommender Systems using Tree Matching

no code implementations15 May 2013 Naseem Biadsy, Lior Rokach, Armin Shmilovici

In the next step we find a correlation between behavior patterns in the source domain and behavior patterns in the target domain.

Recommendation Systems Transfer Learning

Securing Your Transactions: Detecting Anomalous Patterns In XML Documents

no code implementations9 Sep 2012 Eitan Menahem, Alon Schclar, Lior Rokach, Yuval Elovici

XML transactions are used in many information systems to store data and interact with other systems.

Anomaly Detection

Combining One-Class Classifiers via Meta-Learning

no code implementations22 Dec 2011 Eitan Menahem, Lior Rokach, Yuval Elovici

In particular, we propose two new one-class classification performance measures to weigh classifiers and show that a simple ensemble that implements these measures can outperform the most popular one-class ensembles.

General Classification Meta-Learning

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