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2020 – today
- 2024
[j21]Orel Lavie
, Asaf Shabtai, Gilad Katz:
Cost effective transfer of reinforcement learning policies. Expert Syst. Appl. 237(Part A): 121380 (2024)- [j20]Itai Dagan, Roman Vainshtein, Gilad Katz, Lior Rokach:
Automated algorithm selection using meta-learning and pre-trained deep convolution neural networks. Inf. Fusion 105: 102210 (2024) - [j19]Rotem Hananya, Gilad Katz:
Dynamic selection of machine learning models for time-series data. Inf. Sci. 665: 120360 (2024) - 2023
- [c22]Moran Beladev, Gilad Katz, Lior Rokach, Uriel Singer, Kira Radinsky:
GraphERT- Transformers-based Temporal Dynamic Graph Embedding. CIKM 2023: 68-77 - [c21]Liad Giladi, Gilad Katz:
Feedback Decision Transformer: Offline Reinforcement Learning With Feedback. ICDM 2023: 1037-1042 - [i17]Natan Semyonov, Rami Puzis, Asaf Shabtai, Gilad Katz:
ReMark: Receptive Field based Spatial WaterMark Embedding Optimization using Deep Network. CoRR abs/2305.06786 (2023) - [i16]Yizhak Vaisman, Gilad Katz, Yuval Elovici, Asaf Shabtai:
Detecting Anomalous Network Communication Patterns Using Graph Convolutional Networks. CoRR abs/2311.18525 (2023) - 2022
- [j18]Yoni Birman, Shaked Hindi, Gilad Katz, Asaf Shabtai:
Cost-effective ensemble models selection using deep reinforcement learning. Inf. Fusion 77: 133-148 (2022) - [j17]Asaf Harari, Gilad Katz:
Automatic features generation and selection from external sources: A DBpedia use case. Inf. Sci. 582: 398-414 (2022) - [j16]Guy Zaks, Gilad Katz:
ReCom: A deep reinforcement learning approach for semi-supervised tabular data labeling. Inf. Sci. 589: 321-340 (2022) - [j15]Lior Hirsch, Gilad Katz:
Multi-objective pruning of dense neural networks using deep reinforcement learning. Inf. Sci. 610: 381-400 (2022) - [j14]Eli Simhayev, Gilad Katz, Lior Rokach:
Integrated prediction intervals and specific value predictions for regression problems using neural networks. Knowl. Based Syst. 247: 108685 (2022) - [c20]Chen Yanai, Adir Solomon, Gilad Katz, Bracha Shapira, Lior Rokach:
Q-Ball: Modeling Basketball Games Using Deep Reinforcement Learning. AAAI 2022: 8806-8813 - [c19]Asaf Harari, Gilad Katz:
Few-Shot Tabular Data Enrichment Using Fine-Tuned Transformer Architectures. ACL (1) 2022: 1577-1591 - [c18]Hao Wang, Wenjie Qu, Gilad Katz, Wenyu Zhu, Zeyu Gao, Han Qiu, Jianwei Zhuge, Chao Zhang:
jTrans: jump-aware transformer for binary code similarity detection. ISSTA 2022: 1-13 - [i15]Hao Wang, Wenjie Qu, Gilad Katz, Wenyu Zhu, Zeyu Gao, Han Qiu, Jianwei Zhuge, Chao Zhang:
jTrans: Jump-Aware Transformer for Binary Code Similarity. CoRR abs/2205.12713 (2022) - [i14]Orel Lavie, Asaf Shabtai, Gilad Katz:
A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection. CoRR abs/2209.09033 (2022) - 2021
- [j13]Guy Zaks, Gilad Katz:
A Meta Learning-Based Approach for Zero-Shot Co-Training. IEEE Access 9: 146653-146666 (2021) - [j12]Yoni Cohen, Gilad Katz, Lior Rokach:
F-PENN - Forest path encoding for neural networks. Inf. Fusion 75: 186-196 (2021) - [j11]Adir Solomon, Amit Livne, Gilad Katz, Bracha Shapira, Lior Rokach:
Analyzing movement predictability using human attributes and behavioral patterns. Comput. Environ. Urban Syst. 87: 101596 (2021) - [c17]Yoni Birman, Ziv Ido, Gilad Katz, Asaf Shabtai:
Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes. IJCNN 2021: 1-10 - [i13]Yiftach Savransky, Roni Mateless, Gilad Katz:
Secure Machine Learning in the Cloud Using One Way Scrambling by Deconvolution. CoRR abs/2111.03125 (2021) - 2020
- [c16]Guy Zaks, Gilad Katz:
CoMet: A Meta Learning-Based Approach for Cross-Dataset Labeling Using Co-Training. AAMAS 2020: 2068-2070 - [c15]Yoni Birman, Shaked Hindi, Gilad Katz, Asaf Shabtai:
Cost-Effective Malware Detection as a Service Over Serverless Cloud Using Deep Reinforcement Learning. CCGRID 2020: 420-429 - [c14]Moran Beladev, Lior Rokach, Gilad Katz, Ido Guy, Kira Radinsky:
tdGraphEmbed: Temporal Dynamic Graph-Level Embedding. CIKM 2020: 55-64 - [c13]Doron Laadan, Roman Vainshtein, Yarden Curiel, Gilad Katz, Lior Rokach:
MetaTPOT: Enhancing A Tree-based Pipeline Optimization Tool Using Meta-Learning. CIKM 2020: 2097-2100 - [c12]Yuval Heffetz, Roman Vainshtein, Gilad Katz, Lior Rokach:
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering. KDD 2020: 2103-2113 - [i12]Asnat Greenstein-Messica, Roman Vainshtein, Gilad Katz, Bracha Shapira, Lior Rokach:
Automatic Machine Learning Derived from Scholarly Big Data. CoRR abs/2003.03470 (2020) - [i11]Eli Simhayev, Gilad Katz, Lior Rokach:
PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction. CoRR abs/2006.05139 (2020) - [i10]Yoni Birman, Ziv Ido, Gilad Katz, Asaf Shabtai:
Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes. CoRR abs/2007.09256 (2020)
2010 – 2019
- 2019
- [c11]Noy Cohen-Shapira, Lior Rokach, Bracha Shapira, Gilad Katz, Roman Vainshtein:
AutoGRD: Model Recommendation Through Graphical Dataset Representation. CIKM 2019: 821-830 - [i9]Michael Shekasta, Gilad Katz, Asnat Greenstein-Messica, Lior Rokach, Bracha Shapira:
New Item Consumption Prediction Using Deep Learning. CoRR abs/1905.01686 (2019) - [i8]Yoni Birman, Shaked Hindi, Gilad Katz, Asaf Shabtai:
ASPIRE: Automated Security Policy Implementation Using Reinforcement Learning. CoRR abs/1905.10517 (2019) - [i7]Roman Vainshtein, Gilad Katz, Bracha Shapira, Lior Rokach:
Assessing the Quality of Scientific Papers. CoRR abs/1908.04200 (2019) - [i6]Yuval Heffetz, Roman Vainshtein, Gilad Katz, Lior Rokach:
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering. CoRR abs/1911.00061 (2019) - [i5]Doron Laadan, Roman Vainshtein, Yarden Curiel, Gilad Katz, Lior Rokach:
RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines. CoRR abs/1911.00108 (2019) - 2018
- [j10]Gilad Katz, Cornelia Caragea, Asaf Shabtai:
Vertical Ensemble Co-Training for Text Classification. ACM Trans. Intell. Syst. Technol. 9(2): 21:1-21:23 (2018) - [c10]Roman Vainshtein, Asnat Greenstein-Messica, Gilad Katz, Bracha Shapira, Lior Rokach:
A Hybrid Approach for Automatic Model Recommendation. CIKM 2018: 1623-1626 - [i4]Yotam Intrator, Gilad Katz, Asaf Shabtai:
MDGAN: Boosting Anomaly Detection Using Multi-Discriminator Generative Adversarial Networks. CoRR abs/1810.05221 (2018) - 2017
- [j9]Gilad Katz, Lior Rokach:
Wikiometrics: a Wikipedia based ranking system. World Wide Web 20(6): 1153-1177 (2017) - 2016
- [c9]Gilad Katz, Eui Chul Richard Shin, Dawn Song:
ExploreKit: Automatic Feature Generation and Selection. ICDM 2016: 979-984 - [i3]Gilad Katz, Lior Rokach:
Wikiometrics: A Wikipedia Based Ranking System. CoRR abs/1601.01058 (2016) - 2015
- [j8]Gilad Katz, Nir Ofek, Bracha Shapira:
ConSent: Context-based sentiment analysis. Knowl. Based Syst. 84: 162-178 (2015) - [c8]Ron Biton, Gilad Katz, Asaf Shabtai:
Sensor-Based Approach for Predicting Departure Time of Smartphone Users. MOBILESoft 2015: 146-147 - [c7]Nir Ofek, Gilad Katz, Bracha Shapira, Yedidya Bar-Zev:
Sentiment Analysis in Transcribed Utterances. PAKDD (2) 2015: 27-38 - [i2]Gilad Katz, Bracha Shapira:
Enabling Complex Wikipedia Queries - Technical Report. CoRR abs/1508.03298 (2015) - 2014
- [j7]Gilad Katz, Yuval Elovici, Bracha Shapira:
CoBAn: A context based model for data leakage prevention. Inf. Sci. 262: 137-158 (2014) - [j6]Gilad Katz, Asaf Shabtai, Lior Rokach, Nir Ofek:
ConfDTree: A Statistical Method for Improving Decision Trees. J. Comput. Sci. Technol. 29(3): 392-407 (2014) - [c6]Gilad Katz, Anna Shtok, Oren Kurland, Bracha Shapira, Lior Rokach:
Wikipedia-based query performance prediction. SIGIR 2014: 1235-1238 - [p1]Gilad Katz, Asaf Shabtai, Lior Rokach:
Adapted Features and Instance Selection for Improving Co-training. Interactive Knowledge Discovery and Data Mining in Biomedical Informatics 2014: 81-100 - 2013
- [j5]Polina Zilberman, Gilad Katz, Asaf Shabtai, Yuval Elovici:
Analyzing group E-mail exchange to detect data leakage. J. Assoc. Inf. Sci. Technol. 64(9): 1780-1790 (2013) - 2012
- [c5]Michael Fire, Gilad Katz, Yuval Elovici, Bracha Shapira, Lior Rokach:
Predicting Student Exam's Scores by Analyzing Social Network Data. AMT 2012: 584-595 - [c4]Gilad Katz, Asaf Shabtai, Lior Rokach, Nir Ofek:
ConfDTree: Improving Decision Trees Using Confidence Intervals. ICDM 2012: 339-348 - [i1]Gilad Katz, Guy Shani, Bracha Shapira, Lior Rokach:
Using Wikipedia to Boost SVD Recommender Systems. CoRR abs/1212.1131 (2012) - 2011
- [c3]Polina Zilberman, Shlomi Dolev, Gilad Katz, Yuval Elovici, Asaf Shabtai:
Analyzing group communication for preventing data leakage via email. ISI 2011: 37-41 - [c2]Gilad Katz, Nir Ofek, Bracha Shapira, Lior Rokach, Guy Shani:
Using Wikipedia to boost collaborative filtering techniques. RecSys 2011: 285-288
2000 – 2009
- 2009
- [j4]Gilad Katz, Dan Sadot:
Wiener solution of electrical equalizer coefficients in lightwave systems. IEEE Trans. Commun. 57(2): 361-364 (2009) - 2008
- [j3]Gilad Katz, Dan Sadot:
A nonlinear electrical equalizer with decision feedback for OOK optical communication systems. IEEE Trans. Commun. 56(12): 2002-2006 (2008) - 2006
- [j2]Gilad Katz, Dan Sadot, Joseph Tabrikian:
Electrical Dispersion Compensation Equalizers in Optical Direct- and Coherent-Detection Systems. IEEE Trans. Commun. 54(7): 1349-1349 (2006) - [j1]Gilad Katz, Dan Sadot, Joseph Tabrikian:
Electrical Dispersion Compensation Equalizers in Optical Direct- and Coherent-Detection Systems. IEEE Trans. Commun. 54(11): 2045-2050 (2006) - [c1]Gilad Katz, Dan Sadot:
Analytical Solution of Optimal Electrical Equalization Coefficents. ICC 2006: 2749-2754
Coauthor Index
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last updated on 2024-10-07 21:20 CEST by the dblp team
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