Amir Globerson
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 1%
- Computer Networks and Communications top 5%
- Signal Processing top 2%
- Molecular Biology
- Co-authors
- Sam T. RoweisTommi JaakkolaDavid SontagNaftali TishbyRegina BarzilayGal ChechikXavier CarrerasFernando Pereira
- Topics
- Topic Modeling (21 papers)Bayesian Modeling and Causal Inference (19 papers)Machine Learning and Algorithms (18 papers)
- Partner nations
- IsraelUnited StatesCanada
In The Last Decade
Amir Globerson
90 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 127
- Artificial Intelligence 2.0k
- Computer Vision and Pattern Recognition 957
- Computer Networks and Communications 320
- Signal Processing 266
- Molecular Biology 181
Countries citing papers authored by Amir Globerson
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 6 | |
| 3 | 53 | |
| 4 | Regularizing Towards Permutation Invariance In Recurrent Models | 1 |
| 5 | 54 | |
| 6 | Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference | 5 |
| 7 | Predict and Constrain: Modeling Cardinality in Deep Structured Prediction | 3 |
| 8 | Improper Deep Kernels | 0 |
| 9 | Spectral Regularization for Max-Margin Sequence Tagging | 7 |
| 10 | Discrete Chebyshev Classifiers | 5 |
| 11 | Convergence Rate Analysis of MAP Coordinate Minimization Algorithms | 13 |
| 12 | Learning Bayesian Network Structure using LP Relaxations | 100 |
| 13 | More data means less inference: A pseudo-max approach to structured learning | 10 |
| 14 | 87 | |
| 15 | 24 | |
| 16 | Approximate inference using conditional entropy decompositions | 18 |
| 17 | Structured Prediction Models via the Matrix-Tree Theorem | 65 |
| 18 | Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations | 162 |
| 19 | Metric Learning by Collapsing Classesbreakdown → | 436 |
| 20 | 0 |
About Amir Globerson
Amir Globerson is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 94 papers that have together received 2.8k indexed citations. Recurring topics across this work include Topic Modeling (21 papers), Bayesian Modeling and Causal Inference (19 papers) and Machine Learning and Algorithms (18 papers). The work is most often cited by research in Artificial Intelligence (2.0k citations), Computer Vision and Pattern Recognition (957 citations) and Signal Processing (266 citations). Amir Globerson has collaborated with scholars based in Israel, United States and Canada. Frequent 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. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and Nature Neuroscience.
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.