David Saad
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- Model Reduction and Neural Networks 21
- Complex Network Analysis Techniques 19
- Artificial Intelligence top 1%
- Neural Networks and Applications 54
- Machine Learning and ELM 14
- Signal Processing top 2%
- Blind Source Separation Techniques 14
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- Error Correcting Code Techniques 31
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- Cellular Automata and Applications 14
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- DNA and Biological Computing 12
- Co-authors
- Yoshiyuki KabashimaManfred OpperSara A. SollaMTWMagnus RattrayChi Ho YeungK. Y. Michael WongT. Murayama
- Journals
- Physical Review Letters (12 papers)Europhysics Letters (EPL) (6 papers)Journal of Physics A Mathematical and Theoretical (5 papers)
- Partner nations
- United KingdomJapanHong Kong
In The Last Decade
David Saad
134 papers receiving 2.1k citations
Peers
Comparison fields: 5 of 124
- Statistical and Nonlinear Physics 553
- Artificial Intelligence 1.1k
- Signal Processing 301
- Computer Networks and Communications 498
- Computational Theory and Mathematics 252
Countries citing papers authored by David Saad
This map shows the geographic impact of David Saad'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 David Saad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Saad more than expected).
Fields of papers citing papers by David Saad
This network shows the impact of papers produced by David Saad. 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 David Saad. The network helps show where David Saad may publish in the future.
Co-authorship network
The 25 scholars most cited alongside David Saad, 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 | 2025 | 0 | |
| 2 | 2024 | 3 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 2 | |
| 5 | 2022 | 3 | |
| 6 | Message-Passing for Inference and Optimization of Real Variables on Sparse Graphs | 2006 | 2 |
| 7 | 2003 | 2 | |
| 8 | Advanced mean field methods: theory and practice | 2001 | 243 |
| 9 | Error-correcting Codes on a Bethe-like Lattice | 2000 | 1 |
| 10 | Regular and Irregular Gallager-zype Error-Correcting Codes | 1999 | 1 |
| 11 | The Belief in TAP | 1998 | 1 |
| 12 | The Learning Dynamcis of a Universal Approximator | 1996 | 1 |
| 13 | Learning with Noise and Regularizers in Multilayer Neural Networks | 1996 | 6 |
| 14 | Dynamics of On-Line Gradient Descent Learning for Multilayer Neural Networks | 1995 | 10 |
| 15 | Learning from queries for maximum information gain in imperfectly learnable problems | 1994 | 15 |
| 16 | Hyperparameters Evidence and Generalisation for an Unrealisable Rule | 1994 | 1 |
| 17 | Preserving the Diversity of a Genetically Evolving Population of Nets Using the Functional Behavior of Neurons. | 1993 | 2 |
| 18 | Using the Functional Behavior of Neurons for Genetic Recombination in Neural Nets Training. | 1993 | 1 |
| 19 | Training Recurrent Neural Networks via Trajectory Modification. | 1992 | 1 |
| 20 | Training Feed Forward Nets with Binary Weights via a Modified CHIR Algorithm. | 1990 | 9 |
About David Saad
David Saad is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Computer Networks and Communications, Signal Processing and Computational Theory and Mathematics, having authored 141 papers that have together received 2.2k indexed citations. Recurring topics across this work include Neural Networks and Applications (54 papers), Error Correcting Code Techniques (31 papers), Model Reduction and Neural Networks (21 papers), Complex Network Analysis Techniques (19 papers), Machine Learning and ELM (14 papers), Blind Source Separation Techniques (14 papers), Cellular Automata and Applications (14 papers) and DNA and Biological Computing (12 papers). The work is most often cited by research in Statistical and Nonlinear Physics (553 citations), Artificial Intelligence (1.1k citations), Signal Processing (301 citations), Computer Networks and Communications (498 citations) and Computational Theory and Mathematics (252 citations). David Saad has collaborated with scholars based in United Kingdom, Japan and Hong Kong. Frequent co-authors include Yoshiyuki Kabashima, Manfred Opper, Sara A. Solla, MTW, Magnus Rattray, Chi Ho Yeung, K. Y. Michael Wong, T. Murayama, Ido Kanter and Andrey Y. Lokhov. Their work appears in journals such as Physical Review Letters, Europhysics Letters (EPL), Journal of Physics A Mathematical and Theoretical, Physical review. E and Neural Computation.
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.