Hans Peter Graf
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- Face recognition and analysis 10
- Advanced Data Compression Techniques 8
- Advanced Image and Video Retrieval Techniques 8
- Signal Processing top 1%
- Speech and Audio Processing 17
- Artificial Intelligence top 1%
- Neural Networks and Applications 34
- AI in cancer detection 7
- Hardware and Architecture top 5%
- Biophysics top 5%
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- Advanced Memory and Neural Computing 15
- CCD and CMOS Imaging Sensors 13
- Co-authors
- Eric CosattoL. D. JackelGerasimos PotamianosD. HendersonRichard HowardSrimat ChakradharSrihari CadambiLéon Bottou
- Partner nations
- United StatesGermanyJapan
In The Last Decade
Hans Peter Graf
89 papers receiving 2.8k citations
Peers
Comparison fields: 5 of 149
- Computer Vision and Pattern Recognition 1.4k
- Signal Processing 605
- Artificial Intelligence 1.4k
- Hardware and Architecture 137
- Biophysics 82
Countries citing papers authored by Hans Peter Graf
This map shows the geographic impact of Hans Peter Graf'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 Hans Peter Graf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hans Peter Graf more than expected).
Fields of papers citing papers by Hans Peter Graf
This network shows the impact of papers produced by Hans Peter Graf. 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 Hans Peter Graf. The network helps show where Hans Peter Graf may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Hans Peter Graf, 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 | 2023 | 6 | |
| 2 | 2017 | 32 | |
| 3 | 2017 | 16 | |
| 4 | 2009 | 175 | |
| 5 | A Massively Parallel Digital Learning Processor | 2008 | 26 |
| 6 | 2002 | 2 | |
| 7 | 2002 | 9 | |
| 8 | 2000 | 93 | |
| 9 | 1996 | 7 | |
| 10 | Backpropagation without Multiplication | 1993 | 10 |
| 11 | Address Block Location with a Neural Net System | 1993 | 2 |
| 12 | Image Segmentation with Networks of Variable Scales | 1991 | 3 |
| 13 | Reconfigurable Neural Net Chip with 32K Connections | 1990 | 5 |
| 14 | A reconfigurable analog VLSI neural and network | 1990 | 2 |
| 15 | 1990 | 4 | |
| 16 | A Reconfigurable Analog VLSI Neural Network Chip | 1989 | 13 |
| 17 | Neural Network Recognizer for Hand-Written Zip Code Digits | 1988 | 89 |
| 18 | Microelectronic Implementations of Connectionist Neural Networks | 1987 | 4 |
| 19 | 1986 | 32 | |
| 20 | 1974 | 63 |
About Hans Peter Graf
Hans Peter Graf is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 93 papers that have together received 3.0k indexed citations. Recurring topics across this work include Neural Networks and Applications (34 papers), Speech and Audio Processing (17 papers), Advanced Memory and Neural Computing (15 papers), CCD and CMOS Imaging Sensors (13 papers), Face recognition and analysis (10 papers), Advanced Data Compression Techniques (8 papers), Advanced Image and Video Retrieval Techniques (8 papers) and AI in cancer detection (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.4k citations), Signal Processing (605 citations) and Artificial Intelligence (1.4k citations). Hans Peter Graf has collaborated with scholars based in United States, Germany and Japan. Frequent co-authors include Eric Cosatto, L. D. Jackel, Gerasimos Potamianos, D. Henderson, Richard Howard, Srimat Chakradhar, Srihari Cadambi, Léon Bottou, Vladimir Vapnik and J. S. Denker.
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.