S. Della Pietra
- Artificial Intelligence top 2%
- Nuclear and High Energy Physics top 10%
- Computer Vision and Pattern Recognition top 5%
- Statistical and Nonlinear Physics top 5%
- Signal Processing top 5%
- Co-authors
- V. Della PietraJohn LaffertyLuis Álvarez-GauméGregory MooreR. L. MercerSalim RoukosJohn CockePeter Brown
- Topics
- Natural Language Processing Techniques (6 papers)Topic Modeling (5 papers)Advanced Operator Algebra Research (3 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceNuclear Physics BPhysics Letters B
- Partner nations
- United States
In The Last Decade
S. Della Pietra
14 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 95
- Artificial Intelligence 777
- Nuclear and High Energy Physics 201
- Computer Vision and Pattern Recognition 170
- Statistical and Nonlinear Physics 132
- Signal Processing 114
Countries citing papers authored by S. Della Pietra
This map shows the geographic impact of S. Della Pietra'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 S. Della Pietra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Della Pietra more than expected).
Fields of papers citing papers by S. Della Pietra
This network shows the impact of papers produced by S. Della Pietra. 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 S. Della Pietra. The network helps show where S. Della Pietra may publish in the future.
Co-authorship network of co-authors of S. Della Pietra
This figure shows the co-authorship network connecting the top 25 collaborators of S. Della Pietra. A scholar is included among the top collaborators of S. Della Pietra 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 S. Della Pietra. S. Della Pietra is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 13 | |
| 2 | Duality and Auxiliary Functions for Bregman Distances | 29 |
| 3 | 14 | |
| 4 | 1 | |
| 5 | Inducing features of random fieldsbreakdown → | 709 |
| 6 | 47 | |
| 7 | 20 | |
| 8 | 44 | |
| 9 | 154 | |
| 10 | 20 | |
| 11 | 10 | |
| 12 | 1 | |
| 13 | 32 | |
| 14 | 173 |
About S. Della Pietra
S. Della Pietra is a scholar working on Mathematical Physics, Applied Mathematics and Nuclear and High Energy Physics, having authored 14 papers that have together received 1.3k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (6 papers), Topic Modeling (5 papers) and Advanced Operator Algebra Research (3 papers). The work is most often cited by research in Artificial Intelligence (777 citations), Nuclear and High Energy Physics (201 citations) and Statistical and Nonlinear Physics (132 citations). S. Della Pietra has collaborated with scholars based in United States. Frequent co-authors include V. Della Pietra, John Lafferty, Luis Álvarez-Gaumé, Gregory Moore, R. L. Mercer, Salim Roukos, John Cocke, Peter Brown, F. Jelinek and Steven Carlip. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Nuclear Physics B and Physics Letters B.
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.