Niv Haim

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Citations	1372	1363
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Michal IraniProfessor of Computer Science, Weizmann InstituteVerified email at weizmann.ac.il
Yaron LipmanFAIR Meta, Weizmann InstituteVerified email at weizmann.ac.il
Matan AtzmonResearch Scientist, NVIDIAVerified email at nvidia.com
Lior YarivComputer Science PhD student, Weizmann InstituteVerified email at weizmann.ac.il
Gal VardiWeizmann Institute of ScienceVerified email at weizmann.ac.il
Gilad YehudaiPostdoctoral Associate, New York UniversityVerified email at weizmann.ac.il
Amos GroppGraduate Student, Weizmann Institute of ScienceVerified email at weizmann.ac.il
Haggai MaronAssistant Professor at the Technion, Research Scientist at NVIDIA ResearchVerified email at technion.ac.il
Yaniv NikankinVerified email at campus.technion.ac.il
Ohad ShamirWeizmann Institute of ScienceVerified email at weizmann.ac.il
Assaf ShocherPostdoctoral fellow, UC BerkeleyVerified email at berkeley.edu
Heli Ben-HamuPh.D. student, Weizmann Institute of ScienceVerified email at weizmann.ac.il
Tali DekelAssociate Professor, Weizmann Institute of Science; Research Scientist, Google DeepMindVerified email at weizmann.ac.il
Shai BagonResearch Associate, Weizmann Institute of ScienceVerified email at weizmann.ac.il
Niv GranotM.Sc. Student, Weizmann Institute of ScienceVerified email at weizmann.ac.il
Gon BuzagloPrinceton UniversityVerified email at princeton.edu
Boaz KatzAssociate Professor, Weizmann Institute of ScienceVerified email at weizmann.ac.il
Nimrod Segol

Niv Haim

Mobileye

Verified email at mobileye.com - Homepage

Machine Learning Deep Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Implicit geometric regularization for learning shapes A Gropp, L Yariv, N Haim, M Atzmon, Y Lipman Proceedings of the 37th International Conference on Machine Learning (ICML …, 2020	890	2020
Reconstructing training data from trained neural networks N Haim, G Vardi, G Yehudai, O Shamir, M Irani Advances in Neural Information Processing Systems 35, 22911-22924, 2022	164	2022
Controlling neural level sets M Atzmon, N Haim, L Yariv, O Israelov, H Maron, Y Lipman Advances in Neural Information Processing Systems, 2032--2041, 2019	126	2019
Sinfusion: Training diffusion models on a single image or video Y Nikankin, N Haim, M Irani arXiv preprint arXiv:2211.11743, 2022	60	2022
Surface networks via general covers N Haim, N Segol, H Ben-Hamu, H Maron, Y Lipman Proceedings of the IEEE/CVF international conference on computer vision, 632-641, 2019	59	2019
Diverse generation from a single video made possible N Haim, B Feinstein, N Granot, A Shocher, S Bagon, T Dekel, M Irani European Conference on Computer Vision, 491-509, 2022	22*	2022
Deconstructing data reconstruction: Multiclass, weight decay and general losses G Buzaglo, N Haim, G Yehudai, G Vardi, Y Oz, Y Nikankin, M Irani Advances in Neural Information Processing Systems 36, 51515-51535, 2023	20	2023
From discrete to continuous convolution layers A Shocher, B Feinstein, N Haim, M Irani arXiv preprint arXiv:2006.11120, 2020	15	2020
Extreme close approaches in hierarchical triple systems with comparable masses N Haim, B Katz Monthly Notices of the Royal Astronomical Society 479 (3), 3155-3166, 2018	12	2018
Reconstructing training data from real world models trained with transfer learning Y Oz, G Yehudai, G Vardi, I Antebi, M Irani, N Haim arXiv preprint arXiv:2407.15845, 2024	3	2024
Reconstructing Training Data from Multiclass Neural Networks G Buzaglo, N Haim, G Yehudai, G Vardi, M Irani ICLR 2023 Workshop on Pitfalls of limited data and computation for …, 2023	1	2023
TBI: Three-Body Integration N Haim Astrophysics Source Code Library, ascl: 1807.024, 2018		2018

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