Behnam Neyshabur

6.7k total citations
17 papers, 867 citations indexed

About

Behnam Neyshabur is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Behnam Neyshabur has authored 17 papers receiving a total of 867 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 3 papers in Computational Mechanics. Recurrent topics in Behnam Neyshabur's work include Neural Networks and Applications (5 papers), Stochastic Gradient Optimization Techniques (5 papers) and Sparse and Compressive Sensing Techniques (3 papers). Behnam Neyshabur is often cited by papers focused on Neural Networks and Applications (5 papers), Stochastic Gradient Optimization Techniques (5 papers) and Sparse and Compressive Sensing Techniques (3 papers). Behnam Neyshabur collaborates with scholars based in United States, Germany and Canada. Behnam Neyshabur's co-authors include Nathan Srebro, Somaye Hashemifar, Srinadh Bhojanapalli, Aly A. Khan, Jinbo Xu, David McAllester, Seyed Shahriar Arab, Ruslan Salakhutdinov, Ryota Tomioka and Yann LeCun and has published in prestigious journals such as Bioinformatics, arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Behnam Neyshabur

17 papers receiving 824 citations

Peers

Countries citing papers authored by Behnam Neyshabur

Since Specialization

Citations

This map shows the geographic impact of Behnam Neyshabur's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Behnam Neyshabur with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Behnam Neyshabur more than expected).

Fields of papers citing papers by Behnam Neyshabur

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Behnam Neyshabur. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Behnam Neyshabur. The network helps show where Behnam Neyshabur may publish in the future.

Co-authorship network of co-authors of Behnam Neyshabur

This figure shows the co-authorship network connecting the top 25 collaborators of Behnam Neyshabur. A scholar is included among the top collaborators of Behnam Neyshabur based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Behnam Neyshabur. Behnam Neyshabur is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

#	Work	Indexed citations
1	Understanding the effect of sparsity on neural networks robustness 2022 ·arXiv (Cornell University) ·(unknown), (unknown), Hanie Sedghi, Behnam Neyshabur, Olga Saukh	1
2	The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks 2021 ·arXiv (Cornell University) ·(unknown), Hanie Sedghi, Olga Saukh, Behnam Neyshabur	2
3	Fantastic Generalization Measures and Where to Find Them 2020 ·International Conference on Learning Representations ·Yiding Jiang, Behnam Neyshabur, Dilip Krishnan, Hossein Mobahi, Samy Bengio	8
4	What is being transferred in transfer learning 2020 ·Neural Information Processing Systems ·Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang	14
5	The intriguing role of module criticality in the generalization of deep networks 2020 ·International Conference on Learning Representations ·Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi	3
6	Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning 2020 ·Yiding Jiang, (unknown), Manik Sharma, (unknown), (unknown), (unknown), Carlos Lassance, Daniel M. Roy, Gintare Karolina Dziugaite, Suriya Gunasekar, Isabelle Guyon, (unknown), (unknown), Hossein Mobahi, Behnam Neyshabur, Samy Bengio	1
7	The role of over-parametrization in generalization of neural networks 2019 ·Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro	49
8	Predicting protein–protein interactions through sequence-based deep learning 2018 ·Bioinformatics ·Somaye Hashemifar, Behnam Neyshabur, Aly A. Khan, Jinbo Xu	248
9	A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks 2017 ·International Conference on Learning Representations ·Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro	40
10	Exploring Generalization in Deep Learning 2017 ·Neural Information Processing Systems ·Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro	139
11	Global optimality of local search for low rank matrix recovery 2016 ·arXiv (Cornell University) ·Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro	57
12	On Symmetric and Asymmetric LSHs for Inner Product Search 2015 ·International Conference on Machine Learning ·Behnam Neyshabur, Nathan Srebro	32
13	Norm-Based Capacity Control in Neural Networks 2015 ·Conference on Learning Theory ·Behnam Neyshabur, Ryota Tomioka, Nathan Srebro	51
14	Path-SGD: Path-Normalized Optimization in Deep Neural Networks 2015 ·arXiv (Cornell University) ·Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro	59
15	A simpler and better LSH for Maximum Inner Product Search (MIPS) 2014 ·arXiv (Cornell University) ·Behnam Neyshabur, Nathan Srebro	2
16	NETAL: a new graph-based method for global alignment of protein–protein interaction networks 2013 ·Bioinformatics ·Behnam Neyshabur, (unknown), Somaye Hashemifar, Seyed Shahriar Arab	120
17	The Power of Asymmetry in Binary Hashing 2013 ·arXiv (Cornell University) ·Behnam Neyshabur, Payman Yadollahpour, Yury Makarychev, Ruslan Salakhutdinov, (unknown)	41

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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