Amir Globerson

6.9k total citations · 1 hit paper
94 papers, 2.8k citations indexed

About

Amir Globerson is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Amir Globerson has authored 94 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Artificial Intelligence, 22 papers in Computer Vision and Pattern Recognition and 12 papers in Computer Networks and Communications. Recurrent topics in Amir Globerson's work include Topic Modeling (21 papers), Bayesian Modeling and Causal Inference (19 papers) and Machine Learning and Algorithms (18 papers). Amir Globerson is often cited by papers focused on Topic Modeling (21 papers), Bayesian Modeling and Causal Inference (19 papers) and Machine Learning and Algorithms (18 papers). Amir Globerson collaborates with scholars based in Israel, United States and Canada. Amir Globerson's co-authors include Sam T. Roweis, Tommi Jaakkola, David Sontag, Naftali Tishby, Regina Barzilay, Gal Chechik, Xavier Carreras, Fernando Pereira, Terry Koo and Michael Collins and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and Nature Neuroscience.

In The Last Decade

Amir Globerson

90 papers receiving 2.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Metric Learning by Collapsing Classes

2005 436 citations Amir Globerson, Sam T. Roweis Neural Information Processing Systems profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Amir Globerson Israel	26	2.0k	957	320	266	181	94	2.8k
Rong Jin United States	20	1.3k 0.7×	819 0.9×	185 0.6×	179 0.7×	131 0.7×	71	2.2k
M. Mao United States	7	1.6k 0.8×	1.1k 1.1×	393 1.2×	421 1.6×	104 0.6×	18	2.6k
Joseph Keshet Israel	21	1.8k 0.9×	571 0.6×	120 0.4×	493 1.9×	87 0.5×	78	2.3k
Serhii Havrylov Ukraine	3	1.4k 0.7×	803 0.8×	140 0.4×	229 0.9×	122 0.7×	6	2.4k
Chi Wang United States	19	878 0.4×	703 0.7×	156 0.5×	172 0.6×	128 0.7×	72	1.9k
Shiyu Chang United States	34	1.9k 1.0×	2.2k 2.3×	122 0.4×	300 1.1×	110 0.6×	110	3.8k
宏治津田 Japan	1	1.1k 0.5×	867 0.9×	142 0.4×	165 0.6×	70 0.4×	2	2.0k
Xiaofeng He China	22	1.4k 0.7×	847 0.9×	223 0.7×	314 1.2×	324 1.8×	92	2.6k
S. V. N. Vishwanathan United States	25	2.5k 1.3×	1.4k 1.5×	362 1.1×	262 1.0×	575 3.2×	68	3.9k
SingerYoram	6	1.2k 0.6×	644 0.7×	113 0.4×	183 0.7×	100 0.6×	8	1.9k

Countries citing papers authored by Amir Globerson

Since Specialization

Citations

This map shows the geographic impact of Amir Globerson'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 Amir Globerson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amir Globerson more than expected).

Fields of papers citing papers by Amir Globerson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Amir Globerson. 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 Amir Globerson. The network helps show where Amir Globerson may publish in the future.

Co-authorship network of co-authors of Amir Globerson

This figure shows the co-authorship network connecting the top 25 collaborators of Amir Globerson. A scholar is included among the top collaborators of Amir Globerson 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 Amir Globerson. Amir Globerson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Globerson, Amir, et al.. (2024). TREE-G: Decision Trees Contesting Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 38(10). 11032–11042. 2 indexed citations

Herzig, Roei, Leonid Karlinsky, Assaf Arbelle, et al.. (2023). Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs. 14077–14098. 6 indexed citations

Lang, Oran, Gal Elidan, Avinatan Hassidim, et al.. (2021). Explaining in Style: Training a GAN to explain a classifier in StyleSpace. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 673–682. 53 indexed citations

Globerson, Amir, et al.. (2020). Regularizing Towards Permutation Invariance In Recurrent Models. Neural Information Processing Systems. 33. 18364–18374. 1 indexed citations

Globerson, Amir, et al.. (2019). Coreference Resolution with Entity Equalization. 673–677. 54 indexed citations

Globerson, Amir, et al.. (2018). Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference. 5 indexed citations

Globerson, Amir, et al.. (2018). Predict and Constrain: Modeling Cardinality in Deep Structured Prediction. International Conference on Machine Learning. 659–667. 3 indexed citations

Livni, Roi, et al.. (2016). Improper Deep Kernels. International Conference on Artificial Intelligence and Statistics. 1159–1167.

Quattoni, Ariadna, Borja Balle, Xavier Carreras, & Amir Globerson. (2014). Spectral Regularization for Max-Margin Sequence Tagging. QRU Quaderns de Recerca en Urbanisme. 1710–1718. 7 indexed citations

10.

Eban, Elad, et al.. (2014). Discrete Chebyshev Classifiers. International Conference on Machine Learning. 1233–1241. 5 indexed citations

11.

Meshi, Ofer, Amir Globerson, & Tommi Jaakkola. (2012). Convergence Rate Analysis of MAP Coordinate Minimization Algorithms. DSpace@MIT (Massachusetts Institute of Technology). 25. 3014–3022. 13 indexed citations

12.

Jaakkola, Tommi, David Sontag, Amir Globerson, & Marina Meilă. (2010). Learning Bayesian Network Structure using LP Relaxations. DSpace@MIT (Massachusetts Institute of Technology). 9. 358–365. 100 indexed citations

13.

Sontag, David, Ofer Meshi, Amir Globerson, & Tommi Jaakkola. (2010). More data means less inference: A pseudo-max approach to structured learning. DSpace@MIT (Massachusetts Institute of Technology). 23. 2181–2189. 10 indexed citations

14.

Collins, Michael, Amir Globerson, Terry Koo, Xavier Carreras, & Peter L. Bartlett. (2008). Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks. Journal of Machine Learning Research. 9(58). 1775–1822. 87 indexed citations

15.

Stark, Eran, Amir Globerson, Itay Asher, & Moshe Abeles. (2008). Correlations between Groups of Premotor Neurons Carry Information about Prehension. Journal of Neuroscience. 28(42). 10618–10630. 24 indexed citations

16.

Globerson, Amir & Tommi Jaakkola. (2007). Approximate inference using conditional entropy decompositions. International Conference on Artificial Intelligence and Statistics. 130–138. 18 indexed citations

17.

Koo, Terry, Amir Globerson, Xavier Carreras, & Michael Collins. (2007). Structured Prediction Models via the Matrix-Tree Theorem. RECERCAT (Consorci de Serveis Universitaris de Catalunya). 141–150. 65 indexed citations

18.

Globerson, Amir & Tommi Jaakkola. (2007). Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations. Neural Information Processing Systems. 20. 553–560. 162 indexed citations

19.

Globerson, Amir & Sam T. Roweis. (2005). Metric Learning by Collapsing Classes. Neural Information Processing Systems. 18. 451–458. 436 indexed citations breakdown →

20.

Globerson, Amir & Naftali Tishby. (2002). Most informative dimension reduction. National Conference on Artificial Intelligence. 1024–1029.

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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