L. D. Jackel
Impact in
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Face and Expression Recognition
- Artificial Intelligence top 0.2%
- Neural Networks and Applications
Papers in
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- Physics of Superconductivity and Magnetism 17
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- Neural Networks and Applications 24
- Co-authors
- Richard HowardYann LeCunJ. S. DenkerBernhard E. BoserW. HubbardD. HendersonCorinna CortesVladimir Vapnik
- Journals
- Applied Physics Letters (11 papers)IEEE Transactions on Electron Devices (6 papers)IEEE Transactions on Magnetics (5 papers)Journal of Applied Physics (5 papers)Physical Review Letters (4 papers)
- Partner nations
- United StatesGermanyBurundi
In The Last Decade
L. D. Jackel
87 papers receiving 10.9k citations
Hit Papers
Peers
Comparison fields: 5 of 201
- Computer Vision and Pattern Recognition 4.0k
- Artificial Intelligence 3.8k
- Media Technology 847
- Signal Processing 635
- Condensed Matter Physics 532
Countries citing papers authored by L. D. Jackel
This map shows the geographic impact of L. D. Jackel'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 L. D. Jackel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites L. D. Jackel more than expected).
Fields of papers citing papers by L. D. Jackel
This network shows the impact of papers produced by L. D. Jackel. 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 L. D. Jackel. The network helps show where L. D. Jackel may publish in the future.
Co-authors
The 25 scholars most cited alongside L. D. Jackel, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 16 | |
| 2 | 2006 | 86 | |
| 3 | 2002 | 2 | |
| 4 | 1995 | 309 | |
| 5 | Limits on Learning Machine Accuracy Imposed by Data Quality | 1994 | 41 |
| 6 | 1994 | 218 | |
| 7 | Learning Curves: Asymptotic Values and Rate of Convergence | 1993 | 73 |
| 8 | 1993 | 2 | |
| 9 | 1992 | 77 | |
| 10 | 1989 | 80 | |
| 11 | Neural Network Recognizer for Hand-Written Zip Code Digits | 1988 | 89 |
| 12 | 1988 | 6 | |
| 13 | 1986 | 32 | |
| 14 | 1985 | 12 | |
| 15 | 1984 | 33 | |
| 16 | 1983 | 43 | |
| 17 | 1981 | 9 | |
| 18 | 1980 | 1 | |
| 19 | 1979 | 33 | |
| 20 | 1972 | 4 |
About L. D. Jackel
L. D. Jackel is a scholar working on Condensed Matter Physics, Artificial Intelligence, Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 88 papers that have together received 11.5k indexed citations. Recurring topics across this work include Neural Networks and Applications (24 papers), Advancements in Semiconductor Devices and Circuit Design (19 papers), Physics of Superconductivity and Magnetism (17 papers), Semiconductor materials and devices (16 papers), Quantum and electron transport phenomena (15 papers), Handwritten Text Recognition Techniques (12 papers), Advancements in Photolithography Techniques (10 papers) and Advanced Memory and Neural Computing (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (4.0k citations), Artificial Intelligence (3.8k citations), Media Technology (847 citations), Signal Processing (635 citations) and Condensed Matter Physics (532 citations). L. D. Jackel has collaborated with scholars based in United States, Germany and Burundi. Frequent co-authors include Richard Howard, Yann LeCun, J. S. Denker, Bernhard E. Boser, W. Hubbard, D. Henderson, Corinna Cortes, Vladimir Vapnik, Linus A. Fetter and Isabelle Guyon. Their work appears in journals such as Applied Physics Letters, IEEE Transactions on Electron Devices, IEEE Transactions on Magnetics, Journal of Applied Physics and Physical Review Letters.
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.