Jeffrey Mark Siskind

5.8k total citations · 1 hit paper
61 papers, 3.4k citations indexed

About

Jeffrey Mark Siskind is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Jeffrey Mark Siskind has authored 61 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Artificial Intelligence, 28 papers in Computer Vision and Pattern Recognition and 10 papers in Computational Theory and Mathematics. Recurrent topics in Jeffrey Mark Siskind's work include Multimodal Machine Learning Applications (15 papers), Natural Language Processing Techniques (11 papers) and Human Pose and Action Recognition (10 papers). Jeffrey Mark Siskind is often cited by papers focused on Multimodal Machine Learning Applications (15 papers), Natural Language Processing Techniques (11 papers) and Human Pose and Action Recognition (10 papers). Jeffrey Mark Siskind collaborates with scholars based in United States, Ireland and Canada. Jeffrey Mark Siskind's co-authors include Barak A. Pearlmutter, Alexey Radul, Atılım Güneş Baydin, Michael R. Brent, Song Wang, Haonan Yu, N. Siddharth, Andrei Barbu, T. Kubota and David McAllester and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Image Processing.

In The Last Decade

Jeffrey Mark Siskind

60 papers receiving 3.2k citations

Hit Papers

Automatic differentiation in machine learning: a survey 2015 2026 2018 2022 2015 400 800 1.2k

Peers

Jeffrey Mark Siskind
Peter Battaglia United States
Steven J. Nowlan United States
Barak A. Pearlmutter United States
Brenden M. Lake United States
Karen Livescu United States
Thomas Richardson United Kingdom
William R. Mark United States
Jeffrey Mark Siskind
Citations per year, relative to Jeffrey Mark Siskind Jeffrey Mark Siskind (= 1×) peers Geoffrey E. Hinton

Countries citing papers authored by Jeffrey Mark Siskind

Since Specialization
Citations

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

Fields of papers citing papers by Jeffrey Mark Siskind

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jeffrey Mark Siskind. 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 Jeffrey Mark Siskind. The network helps show where Jeffrey Mark Siskind may publish in the future.

Co-authorship network of co-authors of Jeffrey Mark Siskind

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

All Works

20 of 20 papers shown
1.
Ahmed, Hamad, Ronnie Β. Wilbur, Hari Bharadwaj, & Jeffrey Mark Siskind. (2021). Confounds in the Data—Comments on “Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(12). 9217–9220. 9 indexed citations
2.
Siskind, Jeffrey Mark & Barak A. Pearlmutter. (2018). Divide-and-conquer checkpointing for arbitrary \nprograms with no user annotation. MURAL - Maynooth University Research Archive Library (National University of Ireland, Maynooth). 8 indexed citations
3.
Yu, Haonan & Jeffrey Mark Siskind. (2017). Sentence Directed Video Object Codiscovery. International Journal of Computer Vision. 124(3). 312–334. 9 indexed citations
4.
Yu, Haonan, et al.. (2017). Driving Under the Influence (of Language). IEEE Transactions on Neural Networks and Learning Systems. 29(7). 2668–2683. 3 indexed citations
5.
Baydin, Atılım Güneş, Barak A. Pearlmutter, Alexey Radul, & Jeffrey Mark Siskind. (2015). Automatic differentiation in machine learning: a survey. Maynooth University ePrints and eTheses Archive (Maynooth University). 1320 indexed citations breakdown →
6.
Siskind, Jeffrey Mark, et al.. (2015). Action Recognition by Time Series of Retinotopic Appearance and Motion Features. IEEE Transactions on Circuits and Systems for Video Technology. 26(12). 2250–2263. 20 indexed citations
7.
Yu, Haonan & Jeffrey Mark Siskind. (2013). Grounded Language Learning from Video Described with Sentences. Meeting of the Association for Computational Linguistics. 53–63. 62 indexed citations
8.
Barbu, Andrei, N. Siddharth, Haonan Yu, et al.. (2013). Recognize Human Activities from Partially Observed Videos. 2658–2665. 125 indexed citations
9.
Wingate, David, et al.. (2011). Nonstandard Interpretations of Probabilistic Programs for Efficient Inference. Neural Information Processing Systems. 24. 1152–1160. 13 indexed citations
10.
Pollak, Ilya, et al.. (2006). Hierarchical Stochastic Image Grammars for Classification and Segmentation. IEEE Transactions on Image Processing. 15(10). 3033–3052. 27 indexed citations
11.
Siskind, Jeffrey Mark & Barak A. Pearlmutter. (2005). Perturbation Confusion and Referential Transparency:Correct Functional Implementation of Forward-Mode AD. International Journal of Hematology. 99(6). 743–9. 8 indexed citations
12.
Wang, Song, Toshiro Kubota, & Jeffrey Mark Siskind. (2003). Salient Boundary Detection using Ratio Contour. Neural Information Processing Systems. 16. 1571–1578. 14 indexed citations
13.
Siskind, Jeffrey Mark. (2003). Reconstructing force-dynamic models from video sequences. Artificial Intelligence. 151(1-2). 91–154. 21 indexed citations
14.
Fern, Alan, Robert Givan, & Jeffrey Mark Siskind. (2002). Specific-to-general learning for temporal events. National Conference on Artificial Intelligence. 152–158. 4 indexed citations
15.
Fern, Alan, Jeffrey Mark Siskind, & Robert Givan. (2002). Learning temporal, relational, force-dynamic event definitions from video. National Conference on Artificial Intelligence. 159–166. 10 indexed citations
16.
Brent, Michael R. & Jeffrey Mark Siskind. (2001). The role of exposure to isolated words in early vocabulary development. Cognition. 81(2). B33–B44. 324 indexed citations
17.
Siskind, Jeffrey Mark. (2000). Visual Event Classification via Force Dynamics. National Conference on Artificial Intelligence. 149–155. 26 indexed citations
18.
Siskind, Jeffrey Mark. (1994). Lexical acquisition in the presence of noise and homonymy. National Conference on Artificial Intelligence. 760–766. 10 indexed citations
19.
Siskind, Jeffrey Mark & David McAllester. (1993). Nondeterministic lisp as a substrate for constraint logic programming. National Conference on Artificial Intelligence. 133–138. 36 indexed citations
20.
Siskind, Jeffrey Mark. (1992). Naive physics, event perception, lexical semantics, and language acquisition. DSpace@MIT (Massachusetts Institute of Technology). 47 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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