M.J. Weinberger

4.7k total citations · 1 hit paper
79 papers, 3.2k citations indexed

About

M.J. Weinberger is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, M.J. Weinberger has authored 79 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Artificial Intelligence, 30 papers in Computer Vision and Pattern Recognition and 27 papers in Computational Theory and Mathematics. Recurrent topics in M.J. Weinberger's work include Algorithms and Data Compression (58 papers), Advanced Data Compression Techniques (26 papers) and Cellular Automata and Applications (17 papers). M.J. Weinberger is often cited by papers focused on Algorithms and Data Compression (58 papers), Advanced Data Compression Techniques (26 papers) and Cellular Automata and Applications (17 papers). M.J. Weinberger collaborates with scholars based in United States, Israel and Uruguay. M.J. Weinberger's co-authors include G. Seroussi, Guillermo Sapiro, Erik Ordentlich, J. Rissanen, Neri Merhav, Meir Feder, Tsachy Weissman, R. Arps, Sergio Verdú and J. Ziv and has published in prestigious journals such as Proceedings of the IEEE, IEEE Transactions on Information Theory and IEEE Transactions on Image Processing.

In The Last Decade

M.J. Weinberger

75 papers receiving 3.0k 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.

The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

2000 1.1k citations M.J. Weinberger, G. Seroussi et al. IEEE Transactions on Image Processing profile →

Peers

M.J. Weinberger

CVPR

AI

SP

CNC

CTM

G. Seroussi United States

John G. Cleary New Zealand

Alexandr Andoni United States

David L. Neuhoff United States

L.P. Cordella Italy

Xiamu Niu China

W. Equitz United States

Erik Ordentlich United States

Glen G. Langdon United States

Nanrun Zhou China

G. Seroussi United States

M.J. Weinberger

2.3k ×1.0

CVPR

2.2k ×1.5

AI

841 ×0.9

SP

618 ×1.6

CNC

445 ×1.4

CTM

Citations per year, relative to M.J. Weinberger M.J. Weinberger (= 1×) peers G. Seroussi

Countries citing papers authored by M.J. Weinberger

Since Specialization

Citations

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

Fields of papers citing papers by M.J. Weinberger

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M.J. Weinberger. 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 M.J. Weinberger. The network helps show where M.J. Weinberger may publish in the future.

Co-authorship network of co-authors of M.J. Weinberger

This figure shows the co-authorship network connecting the top 25 collaborators of M.J. Weinberger. A scholar is included among the top collaborators of M.J. Weinberger 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 M.J. Weinberger. M.J. Weinberger 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

1.

Seroussi, G. & M.J. Weinberger. (2013). Twice-universal fixed to variable-length random number generators for finite memory sources. 52. 634–638. 1 indexed citations

2.

Motta, Gaetano, Erik Ordentlich, Ignacio Ramírez, G. Seroussi, & M.J. Weinberger. (2010). The iDUDE Framework for Grayscale Image Denoising. IEEE Transactions on Image Processing. 20(1). 1–21. 46 indexed citations

3.

Szpankowski, Wojciech & M.J. Weinberger. (2010). Minimax redundancy for large alphabets. 23. 1488–1492. 8 indexed citations

4.

Motta, Giovanni, Erik Ordentlich, & M.J. Weinberger. (2008). Defect list compression. 1000–1004. 1 indexed citations

5.

Grünwald, Peter, Petri Myllymäki, Ioan Tăbuş, M.J. Weinberger, & Bin Yu. (2008). Festschrift in honor of Jorma Rissanen on the occasion of his 75th birthday. Data Archiving and Networked Services (DANS). 2 indexed citations

6.

Seroussi, G., et al.. (2007). Type Classes of Tree Models. 886–890. 5 indexed citations

7.

Merhav, Neri & M.J. Weinberger. (2006). Universal Simulation with a Fidelity Criterion. 2521–2525. 1 indexed citations

8.

Motta, Gaetano, Erik Ordentlich, Ignacio Ramírez, G. Seroussi, & M.J. Weinberger. (2005). The DUDE framework for continuous tone image denoising. III–345. 17 indexed citations

9.

Ordentlich, Erik, Tsachy Weissman, M.J. Weinberger, Anelia Somekh-Baruch, & Neri Merhav. (2004). Discrete universal filtering through incremental parsing. 352–361. 15 indexed citations

10.

Weissman, Tsachy, Erik Ordentlich, G. Seroussi, Sergio Verdú, & M.J. Weinberger. (2003). Inequalities for the L1 Deviation of the Empirical Distribution. The Netherlands Journal of Medicine. 69(7). 330–4. 74 indexed citations

11.

Weinberger, M.J., J. Ziv, & A. Lempel. (2002). On the optimal asymptotic performance of universal ordering and discrimination of individual sequences. 239–246. 3 indexed citations

12.

Ordentlich, Erik, M.J. Weinberger, & G. Seroussi. (2002). On modeling and ordering for embedded image coding. 8. 297–297. 4 indexed citations

13.

Merhav, Neri, G. Seroussi, & M.J. Weinberger. (2002). Universal probability assignment in the class of two-sided geometric distributions. 70–70.

14.

Motta, Giovanni & M.J. Weinberger. (2001). Compression of Polynomial Texture Maps. 2 indexed citations

15.

Weinberger, M.J., G. Seroussi, & Guillermo Sapiro. (2000). The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS. IEEE Transactions on Image Processing. 9(8). 1309–1324. 1119 indexed citations breakdown →

16.

Weinberger, M.J. & G. Seroussi. (1997). Sequential prediction and ranking in universal context modeling and data compression. IEEE Transactions on Information Theory. 43(5). 1697–1706. 21 indexed citations

17.

Weinberger, M.J., J. Rissanen, & R. Arps. (1996). Applications of universal context modeling to lossless compression of gray-scale images. IEEE Transactions on Image Processing. 5(4). 575–586. 158 indexed citations

18.

Weinberger, M.J., Neri Merhav, & Meir Feder. (1994). Optimal sequential probability assignment for individual sequences. IEEE Transactions on Information Theory. 40(2). 384–396. 78 indexed citations

19.

Weinberger, M.J., et al.. (1992). A sequential algorithm for the universal coding of finite memory sources. IEEE Transactions on Information Theory. 38(3). 1002–1014. 68 indexed citations

20.

Weinberger, M.J. & A. Lempel. (1990). Factorization of symmetric circulant matrices in finite fields. Discrete Applied Mathematics. 28(3). 271–285. 2 indexed citations

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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