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ORCIDLior Rokach
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- affiliation: Ben-Gurion University, Beersheba, Israel
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2020 – today
- 2024
[j139]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)- [j138]Noy Cohen-Shapira, Lior Rokach:
PnT: Born-again tree-based model via fused decision path encoding. Inf. Fusion 112: 102545 (2024) - [j137]Seffi Cohen, Edo Lior, Moshe Bocher, Lior Rokach:
Improving severity classification of Hebrew PET-CT pathology reports using test-time augmentation. J. Biomed. Informatics 149: 104577 (2024) - [j136]Amiel Meiseles, Lior Rokach:
Iterative Feature eXclusion (IFX): Mitigating feature starvation in gradient boosted decision trees. Knowl. Based Syst. 289: 111546 (2024) - [j135]Ronen Priel, Lior Rokach:
Machine learning-based stock picking using value investing and quality features. Neural Comput. Appl. 36(20): 11963-11986 (2024) - [c121]Seffi Cohen, Ofir Arbili, Yisroel Mirsky, Lior Rokach:
TTTS: Tree Test Time Simulation for Enhancing Decision Tree Robustness against Adversarial Examples. AAAI 2024: 20993-21000 - [c120]Nurit Cohen-Inger, Guy Rozenblatt, Seffi Cohen, Lior Rokach, Bracha Shapira:
FairUS - UpSampling Optimized Method for Boosting Fairness. ECAI 2024: 962-970 - [i59]Seffi Cohen, Lior Rokach:
BagStacking: An Integrated Ensemble Learning Approach for Freezing of Gait Detection in Parkinson's Disease. CoRR abs/2402.17783 (2024) - [i58]Shai Meital, Lior Rokach, Roman Vainshtein, Nir Grinberg:
The Branch Not Taken: Predicting Branching in Online Conversations. CoRR abs/2404.13613 (2024) - [i57]Koren Ishlach, Itzhak Ben-David, Michael Fire, Lior Rokach:
A Novel Method for News Article Event-Based Embedding. CoRR abs/2405.13071 (2024) - 2023
- [j134]Amiel Meiseles, Yair Motro, Lior Rokach, Jacob Moran-Gilad:
Vulnerability of pangolin SARS-CoV-2 lineage assignment to adversarial attack. Artif. Intell. Medicine 146: 102722 (2023) - [j133]Maxim Bragilovski, Zahi Kapri, Lior Rokach, Shelly Levy-Tzedek:
TLTD: Transfer Learning for Tabular Data. Appl. Soft Comput. 147: 110748 (2023) - [j132]Eyal Mazuz, Guy Shtar, Nir Kutsky, Lior Rokach, Bracha Shapira:
Pretrained transformer models for predicting the withdrawal of drugs from the market. Bioinform. 39(8) (2023) - [j131]Seffi Cohen, Niv Goldshlager, Bracha Shapira, Lior Rokach:
TTANAD: Test-Time Augmentation for Network Anomaly Detection. Entropy 25(5): 820 (2023) - [j130]Inbal Roshanski, Meir Kalech, Lior Rokach:
Automatic Feature Engineering for Learning Compact Decision Trees. Expert Syst. Appl. 229(Part A): 120470 (2023) - [j129]Liat Antwarg, Chen Galed, Nathaniel Shimoni, Lior Rokach, Bracha Shapira:
Shapley-based feature augmentation. Inf. Fusion 96: 92-102 (2023) - [j128]Seffi Cohen, Dan Presil, Or Katz, Ofir Arbili, Shvat Messica, Lior Rokach:
Enhancing social network hate detection using back translation and GPT-3 augmentations during training and test-time. Inf. Fusion 99: 101887 (2023) - [j127]Seffi Cohen, Niv Goldshlager, Lior Rokach, Bracha Shapira:
Boosting anomaly detection using unsupervised diverse test-time augmentation. Inf. Sci. 626: 821-836 (2023) - [j126]Ariel Bar, Bracha Shapira, Lior Rokach:
Context aware Markov chains models. Knowl. Based Syst. 282: 111083 (2023) - [j125]Adva Hadrian, Roman Vainshtein, Bracha Shapira, Lior Rokach:
DeepCAN: Hybrid Method for Road Type Classification Using Vehicle Sensor Data for Smart Autonomous Mobility. IEEE Trans. Intell. Transp. Syst. 24(11): 11756-11772 (2023) - [c119]Moran Beladev, Gilad Katz, Lior Rokach, Uriel Singer, Kira Radinsky:
GraphERT- Transformers-based Temporal Dynamic Graph Embedding. CIKM 2023: 68-77 - [c118]Tamar Didi, Ido Guy, Amit Livne, Arnon Dagan, Lior Rokach, Bracha Shapira:
Promoting Tail Item Recommendations in E-Commerce. UMAP 2023: 194-203 - [d1]Shani Cohen, Lior Rokach, Isana Veksler-Lublinsky:
Dataset of GraphRNA scores for sRNA-mRNA interactions in E. coli. Zenodo, 2023
- [i56]Tzvi Lederer, Gallil Maimon, Lior Rokach:
Silent Killer: Optimizing Backdoor Trigger Yields a Stealthy and Powerful Data Poisoning Attack. CoRR abs/2301.02615 (2023) - [i55]Bar Vered, Guy Shtar, Lior Rokach, Bracha Shapira:
AMFPMC - An improved method of detecting multiple types of drug-drug interactions using only known drug-drug interactions. CoRR abs/2302.03355 (2023) - 2022
- [j124]Gal Rozenfeld, Meir Kalech, Lior Rokach:
Active-learning-based reconstruction of circuit model. Appl. Intell. 52(5): 5125-5143 (2022) - [j123]Guy Shtar, Lior Rokach, Bracha Shapira, Elkana Kohn, Matitiahu Berkovitch, Maya Berlin:
Explainable multimodal machine learning model for classifying pregnancy drug safety. Bioinform. 38(4): 1102-1109 (2022) - [j122]Guy Shtar, Asnat Greenstein-Messica, Eyal Mazuz, Lior Rokach, Bracha Shapira:
Predicting drug characteristics using biomedical text embedding. BMC Bioinform. 23(1): 526 (2022) - [j121]Amiel Meiseles, Denis Paley, Mira Ziv, Yarin Hadid, Lior Rokach, Tamar Tadmor:
Explainable machine learning for chronic lymphocytic leukemia treatment prediction using only inexpensive tests. Comput. Biol. Medicine 145: 105490 (2022) - [j120]Adir Solomon, Bracha Shapira, Lior Rokach:
Predicting application usage based on latent contextual information. Comput. Commun. 192: 197-209 (2022) - [j119]Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach:
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain. ACM Comput. Surv. 54(5): 108:1-108:36 (2022) - [j118]Amit Livne, Eliad Shem Tov, Adir Solomon, Achiya Elyasaf, Bracha Shapira, Lior Rokach:
Evolving context-aware recommender systems with users in mind. Expert Syst. Appl. 189: 116042 (2022) - [j117]Adir Solomon, Mor Kertis, Bracha Shapira, Lior Rokach:
A deep learning framework for predicting burglaries based on multiple contextual factors. Expert Syst. Appl. 199: 117042 (2022) - [j116]Yael Mathov, Lior Rokach, Yuval Elovici:
Enhancing real-world adversarial patches through 3D modeling of complex target scenes. Neurocomputing 499: 11-22 (2022) - [j115]Shir Chorev, Philip Tannor, Dan Ben Israel, Noam Bressler, Itay Gabbay, Nir Hutnik, Jonatan Liberman, Matan Perlmutter, Yurii Romanyshyn, Lior Rokach:
Deepchecks: A Library for Testing and Validating Machine Learning Models and Data. J. Mach. Learn. Res. 23: 285:1-285:6 (2022) - [j114]Noy Cohen-Shapira, Lior Rokach:
Learning dataset representation for automatic machine learning algorithm selection. Knowl. Inf. Syst. 64(10): 2599-2635 (2022) - [j113]Adir Solomon, Michael Michaelshvili, Ron Bitton, Bracha Shapira, Lior Rokach, Rami Puzis, Asaf Shabtai:
Contextual security awareness: A context-based approach for assessing the security awareness of users. Knowl. Based Syst. 246: 108709 (2022) - [j112]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) - [j111]Gallil Maimon, Lior Rokach:
A universal adversarial policy for text classifiers. Neural Networks 153: 282-291 (2022) - [j110]Ben Nassi, Jacob Shams, Lior Rokach, Yuval Elovici:
Virtual Breathalyzer: Towards the Detection of Intoxication Using Motion Sensors of Commercial Wearable Devices. Sensors 22(9): 3580 (2022) - [c117]Chen Yanai, Adir Solomon, Gilad Katz, Bracha Shapira, Lior Rokach:
Q-Ball: Modeling Basketball Games Using Deep Reinforcement Learning. AAAI 2022: 8806-8813 - [c116]Yarden Rotem, Nathaniel Shimoni, Lior Rokach, Bracha Shapira:
Transfer Learning for Time Series Classification Using Synthetic Data Generation. CSCML 2022: 232-246 - [e9]Francesco Ricci, Lior Rokach, Bracha Shapira:
Recommender Systems Handbook. Springer US 2022, ISBN 978-1-0716-2196-7 [contents] - [r5]Francesco Ricci, Lior Rokach, Bracha Shapira:
Recommender Systems: Techniques, Applications, and Challenges. Recommender Systems Handbook 2022: 1-35 - [i54]Shir Chorev, Philip Tannor, Dan Ben Israel, Noam Bressler, Itay Gabbay, Nir Hutnik, Jonatan Liberman, Matan Perlmutter, Yurii Romanyshyn, Lior Rokach:
Deepchecks: A Library for Testing and Validating Machine Learning Models and Data. CoRR abs/2203.08491 (2022) - [i53]Gallil Maimon, Lior Rokach:
A Universal Adversarial Policy for Text Classifiers. CoRR abs/2206.09458 (2022) - [i52]Yarden Rotem, Nathaniel Shimoni, Lior Rokach, Bracha Shapira:
Transfer learning for time series classification using synthetic data generation. CoRR abs/2207.07897 (2022) - [i51]Moti Cohen, Lior Rokach, Rami Puzis:
Cross Version Defect Prediction with Class Dependency Embeddings. CoRR abs/2212.14404 (2022) - 2021
- [j109]Seffi Cohen, Noa Dagan, Nurit Cohen-Inger, Dan Ofer, Lior Rokach:
ICU Survival Prediction Incorporating Test-Time Augmentation to Improve the Accuracy of Ensemble-Based Models. IEEE Access 9: 91584-91592 (2021) - [j108]Shani Cohen, Lior Rokach, Yair Motro, Jacob Moran-Gilad, Isana Veksler-Lublinsky:
minMLST: machine learning for optimization of bacterial strain typing. Bioinform. 37(3): 303-311 (2021) - [j107]Liat Antwarg, Ronnie Mindlin Miller, Bracha Shapira, Lior Rokach:
Explaining anomalies detected by autoencoders using Shapley Additive Explanations. Expert Syst. Appl. 186: 115736 (2021) - [j106]Yoni Cohen, Gilad Katz, Lior Rokach:
F-PENN - Forest path encoding for neural networks. Inf. Fusion 75: 186-196 (2021) - [j105]Omer Sagi, Lior Rokach:
Approximating XGBoost with an interpretable decision tree. Inf. Sci. 572: 522-542 (2021) - [j104]Itay Gabbay, Bracha Shapira, Lior Rokach:
Isolation forests and landmarking-based representations for clustering algorithm recommendation using meta-learning. Inf. Sci. 574: 473-489 (2021) - [j103]Noy Cohen-Shapira, Lior Rokach:
Automatic selection of clustering algorithms using supervised graph embedding. Inf. Sci. 577: 824-851 (2021) - [j102]Itay Hazan, Oded Margalit, Lior Rokach:
Supporting unknown number of users in keystroke dynamics models. Knowl. Based Syst. 221: 106982 (2021) - [j101]Shir Kashi, Ronit Feingold Polak, Boaz Lerner, Lior Rokach, Shelly Levy-Tzedek:
A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients. IEEE Trans. Emerg. Top. Comput. 9(3): 1234-1247 (2021) - [j100]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) - [c115]Amit Livne, Roy Dor, Bracha Shapira, Lior Rokach:
BNN: Boosting Neural Network Framework Utilizing Limited Amount of Data. CIKM 2021: 1150-1159 - [c114]Noy Cohen-Shapira, Lior Rokach:
TRIO: Task-agnostic dataset representation optimized for automatic algorithm selection. ICDM 2021: 81-90 - [c113]Ori Or-Meir, Aviad Cohen, Yuval Elovici, Lior Rokach, Nir Nissim:
Pay Attention: Improving Classification of PE Malware Using Attention Mechanisms Based on System Call Analysis. IJCNN 2021: 1-8 - [c112]Nir Regev, Lior Rokach, Asaf Shabtai:
Approximating Aggregated SQL Queries with LSTM Networks. IJCNN 2021: 1-8 - [c111]Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach:
Sequence Squeezing: A Defense Method Against Adversarial Examples for API Call-Based RNN Variants. IJCNN 2021: 1-10 - [i50]Yael Mathov, Lior Rokach, Yuval Elovici:
Enhancing Real-World Adversarial Patches with 3D Modeling Techniques. CoRR abs/2102.05334 (2021) - [i49]Seffi Cohen, Niv Goldshlager, Lior Rokach, Bracha Shapira:
Boosting Anomaly Detection Using Unsupervised Diverse Test-Time Augmentation. CoRR abs/2110.15700 (2021) - 2020
- [j99]Amiel Meiseles, Lior Rokach:
Source Model Selection for Deep Learning in the Time Series Domain. IEEE Access 8: 6190-6200 (2020) - [j98]Asnat Greenstein-Messica, Lior Rokach:
Machine learning and operation research based method for promotion optimization of products with no price elasticity history. Electron. Commer. Res. Appl. 40: 100914 (2020) - [j97]Itay Hazan, Oded Margalit, Lior Rokach:
Keystroke dynamics obfuscation using key grouping. Expert Syst. Appl. 143 (2020) - [j96]Adam Kubany, Shimon Ben Ishay, Ruben-sacha Ohayon, Armin Shmilovici, Lior Rokach, Tomer Doitshman:
Comparison of state-of-the-art deep learning APIs for image multi-label classification using semantic metrics. Expert Syst. Appl. 161: 113656 (2020) - [j95]Omer Sagi, Lior Rokach:
Explainable decision forest: Transforming a decision forest into an interpretable tree. Inf. Fusion 61: 124-138 (2020) - [j94]Sergio González, Salvador García, Javier Del Ser, Lior Rokach, Francisco Herrera:
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities. Inf. Fusion 64: 205-237 (2020) - [j93]Ido Tam, Meir Kalech, Lior Rokach, Eyal Madar, Jacob Bortman, Renata Klein:
Probability-Based Algorithm for Bearing Diagnosis with Untrained Spall Sizes. Sensors 20(5): 1298 (2020) - [c110]Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach:
Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers. ACSAC 2020: 611-626 - [c109]Amiel Meiseles, Ishai Rosenberg, Yair Motro, Lior Rokach, Jacob Moran-Gilad:
Adversarial Vulnerability of Deep Learning Models in Analyzing Next Generation Sequencing Data. BIBM 2020: 464-468 - [c108]Moran Beladev, Lior Rokach, Gilad Katz, Ido Guy, Kira Radinsky:
tdGraphEmbed: Temporal Dynamic Graph-Level Embedding. CIKM 2020: 55-64 - [c107]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 - [c106]Adir Solomon, Amit Magen, Simo Hanouna, Mor Kertis, Bracha Shapira, Lior Rokach:
Crime Linkage Based on Textual Hebrew Police Reports Utilizing Behavioral Patterns. CIKM 2020: 2749-2756 - [c105]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 - [c104]Hagit Grushka-Cohen, Ofer Biller, Oded Sofer, Lior Rokach, Bracha Shapira:
Using Bandits for Effective Database Activity Monitoring. PAKDD (2) 2020: 701-713 - [c103]Hen Tzaban, Ido Guy, Asnat Greenstein-Messica, Arnon Dagan, Lior Rokach, Bracha Shapira:
Product Bundle Identification using Semi-Supervised Learning. SIGIR 2020: 791-800 - [i48]Sigal Shaked, Amos Zamir, Roman Vainshtein, Moshe Unger, Lior Rokach, Rami Puzis, Bracha Shapira:
Sequence Preserving Network Traffic Generation. CoRR abs/2002.09832 (2020) - [i47]Sigal Shaked, Lior Rokach:
PrivGen: Preserving Privacy of Sequences Through Data Generation. CoRR abs/2002.09834 (2020) - [i46]Asnat Greenstein-Messica, Roman Vainshtein, Gilad Katz, Bracha Shapira, Lior Rokach:
Automatic Machine Learning Derived from Scholarly Big Data. CoRR abs/2003.03470 (2020) - [i45]Eli Simhayev, Gilad Katz, Lior Rokach:
PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction. CoRR abs/2006.05139 (2020) - [i44]Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach:
Adversarial Learning in the Cyber Security Domain. CoRR abs/2007.02407 (2020) - [i43]Amit Livne, Roy Dor, Eyal Mazuz, Tamar Didi, Bracha Shapira, Lior Rokach:
Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate. CoRR abs/2007.13087 (2020) - [i42]Hagit Grushka-Cohen, Raphael Cohen, Bracha Shapira, Jacob Moran-Gilad, Lior Rokach:
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach. CoRR abs/2007.14805 (2020) - [i41]Amit Livne, Eliad Shem Tov, Adir Solomon, Achiya Elyasaf, Bracha Shapira, Lior Rokach:
Evolving Context-Aware Recommender Systems With Users in Mind. CoRR abs/2007.15409 (2020) - [i40]Victor Makarenkov, Lior Rokach:
Lessons Learned from Applying off-the-shelf BERT: There is no Silver Bullet. CoRR abs/2009.07238 (2020) - [i39]Nir Regev, Lior Rokach, Asaf Shabtai:
Approximating Aggregated SQL Queries With LSTM Networks. CoRR abs/2010.13149 (2020) - [i38]Noy Cohen-Shapira, Lior Rokach:
Automatic selection of clustering algorithms using supervised graph embedding. CoRR abs/2011.08225 (2020) - [i37]Ben Nassi, Lior Rokach, Yuval Elovici:
The Age of Testifying Wearable Devices: The Case of Intoxication Detection. IACR Cryptol. ePrint Arch. 2020: 1504 (2020)
2010 – 2019
- 2019
- [b7]Lior Rokach:
Ensemble Learning - Pattern Classification Using Ensemble Methods, 2nd Edition. Series in Machine Perception and Artificial Intelligence 85, WorldScientific 2019, ISBN 9789811201950, pp. 1-300 - [j92]Nir Nissim, Aviad Cohen, Jian Wu, Andrea Lanzi, Lior Rokach, Yuval Elovici, C. Lee Giles:
Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework. IEEE Access 7: 110050-110073 (2019) - [j91]Itay Hazan, Oded Margalit, Lior Rokach:
Securing keystroke dynamics from replay attacks. Appl. Soft Comput. 85 (2019) - [j90]Nir Nissim, Omri Lahav, Aviad Cohen, Yuval Elovici, Lior Rokach:
Volatile memory analysis using the MinHash method for efficient and secured detection of malware in private cloud. Comput. Secur. 87 (2019) - [j89]Ori Or-Meir, Nir Nissim, Yuval Elovici, Lior Rokach:
Dynamic Malware Analysis in the Modern Era - A State of the Art Survey. ACM Comput. Surv. 52(5): 88:1-88:48 (2019) - [j88]Victor Makarenkov, Lior Rokach, Bracha Shapira:
Choosing the right word: Using bidirectional LSTM tagger for writing support systems. Eng. Appl. Artif. Intell. 84: 1-10 (2019) - [j87]Abraham Israeli, Lior Rokach, Asaf Shabtai:
Constraint learning based gradient boosting trees. Expert Syst. Appl. 128: 287-300 (2019) - [j86]Victor Makarenkov, Ido Guy, Niva Hazon, Tamar Meisels, Bracha Shapira, Lior Rokach:
Implicit dimension identification in user-generated text with LSTM networks. Inf. Process. Manag. 56(5): 1880-1893 (2019) - [j85]Guy Shtar, Bracha Shapira, Lior Rokach:
Clustering Wi-Fi fingerprints for indoor-outdoor detection. Wirel. Networks 25(3): 1341-1359 (2019) - [c102]Noy Cohen-Shapira, Lior Rokach, Bracha Shapira, Gilad Katz, Roman Vainshtein:
AutoGRD: Model Recommendation Through Graphical Dataset Representation. CIKM 2019: 821-830 - [c101]Philip Tannor, Lior Rokach:
AugBoost: Gradient Boosting Enhanced with Step-Wise Feature Augmentation. IJCAI 2019: 3555-3561 - [i36]Victor Makarenkov, Lior Rokach, Bracha Shapira:
Choosing the Right Word: Using Bidirectional LSTM Tagger for Writing Support Systems. CoRR abs/1901.02490 (2019) - [i35]Victor Makarenkov, Ido Guy, Niva Hazon, Tamar Meisels, Bracha Shapira, Lior Rokach:
Implicit Dimension Identification in User-Generated Text with LSTM Networks. CoRR abs/1901.09219 (2019) - [i34]Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach:
Defense Methods Against Adversarial Examples for Recurrent Neural Networks. CoRR abs/1901.09963 (2019) - [i33]Liat Antwarg, Bracha Shapira, Lior Rokach:
Explaining Anomalies Detected by Autoencoders Using SHAP. CoRR abs/1903.02407 (2019) - [i32]Guy Shtar, Lior Rokach, Bracha Shapira:
Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures. CoRR abs/1903.04571 (2019) - [i31]Saar Tal, Bracha Shapira, Lior Rokach:
Personal Dynamic Cost-Aware Sensing for Latent Context Detection. CoRR abs/1903.05376 (2019) - [i30]Eran Fainman, Bracha Shapira, Lior Rokach, Yisroel Mirsky:
Online Budgeted Learning for Classifier Induction. CoRR abs/1903.05382 (2019) - [i29]Adam Kubany, Shimon Ben Ishay, Ruben-sacha Ohayon, Armin Shmilovici, Lior Rokach, Tomer Doitshman:
Semantic Comparison of State-of-the-Art Deep Learning Methods for Image Multi-Label Classification. CoRR abs/1903.09190 (2019) - [i28]Michael Shekasta, Gilad Katz, Asnat Greenstein-Messica, Lior Rokach, Bracha Shapira:
New Item Consumption Prediction Using Deep Learning. CoRR abs/1905.01686 (2019) - [i27]Guy Shtar, Bracha Shapira, Lior Rokach:
Clustering Wi-Fi Fingerprints for Indoor-Outdoor Detection. CoRR abs/1908.00758 (2019) - [i26]Roman Vainshtein, Gilad Katz, Bracha Shapira, Lior Rokach:
Assessing the Quality of Scientific Papers. CoRR abs/1908.04200 (2019) - [i25]Amit Livne, Moshe Unger, Bracha Shapira, Lior Rokach:
Deep Context-Aware Recommender System Utilizing Sequential Latent Context. CoRR abs/1909.03999 (2019) - [i24]Hagit Grushka-Cohen, Ofer Biller, Oded Sofer, Lior Rokach, Bracha Shapira:
Diversifying Database Activity Monitoring with Bandits. CoRR abs/1910.10777 (2019) - [i23]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) - [i22]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
- [j84]Ron Bitton, Andrey Finkelshtein, Lior Sidi, Rami Puzis, Lior Rokach, Asaf Shabtai:
Taxonomy of mobile use