David Barber
Impact in
- Artificial Intelligence top 0.5%
- Gaussian Processes and Bayesian Inference
- Neural Networks and Applications
- Anomaly Detection Techniques and Applications
- Bayesian Modeling and Causal Inference
- Bayesian Methods and Mixture Models
- Signal Processing top 2%
- Speech and Audio Processing
- Music and Audio Processing
Papers in
-
- Gaussian Processes and Bayesian Inference 22
- Neural Networks and Applications 21
- Bayesian Methods and Mixture Models 10
- Bayesian Modeling and Causal Inference 10
- Machine Learning and Algorithms 7
- Target Tracking and Data Fusion in Sensor Networks 7
-
- Blind Source Separation Techniques 11
- Music and Audio Processing 6
- Co-authors
- Christopher K. I. Williams (2 shared papers)Felix Agakov (4 shared papers)Chris Bishop (3 shared papers)Ali Taylan Cemgil (3 shared papers)Herwig Immervoll (2 shared papers)Hilbert J. Kappen (2 shared papers)Aleksandar Botev (5 shared papers)Hippolyt Ritter (6 shared papers)
- Journals
- Neural Computation (3 papers)Journal of Machine Learning Research (3 papers)Europhysics Letters (EPL) (2 papers)IEEE Transactions on Audio Speech and Language Processing (2 papers)IEEE Signal Processing Letters (2 papers)
- Partner nations
- United KingdomSwitzerlandNetherlands
In The Last Decade
David Barber
82 papers receiving 2.6k citations
David Barber's Hit Papers
Peers
Comparison fields: 5 of 179
- Artificial Intelligence 1.5k
- Signal Processing 412
- Computer Vision and Pattern Recognition 477
- Statistics and Probability 165
- Statistics, Probability and Uncertainty 122
Countries citing papers authored by David Barber
This map shows the geographic impact of David Barber'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 Barber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Barber more than expected).
Fields of papers citing papers by David Barber
This network shows the impact of papers produced by David Barber. 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 Barber. The network helps show where David Barber may publish in the future.
Co-authors
The 25 scholars most cited alongside David Barber, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 87 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Bayesian Reasoning and Machine Learning Hit paper breakdown → | 2012 | 937 |
| 2 | Bayesian classification with Gaussian processes Hit paper breakdown → | 1998 | 513 |
| 3 | The IM algorithm: a variational approach to Information Maximization | 2003 | 120 |
| 4 | 2011 | 95 | |
| 5 | 2006 | 90 | |
| 6 | Ensemble learning in Bayesian neural networks | 1998 | 85 |
| 7 | 2006 | 82 | |
| 8 | Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems | 2006 | 59 |
| 9 | A Scalable Laplace Approximation for Neural Networks | 2018 | 51 |
| 10 | 1980 | 49 | |
| 11 | 2010 | 49 | |
| 12 | 2007 | 42 | |
| 13 | Ensemble Learning for Multi-Layer Networks | 1997 | 41 |
| 14 | Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo | 1996 | 35 |
| 15 | 2014 | 33 | |
| 16 | Gaussian Kullback-Leibler approximate inference | 2013 | 31 |
| 17 | Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting | 2018 | 26 |
| 18 | 2006 | 24 | |
| 19 | Can parents afford to work | 2005 | 24 |
| 20 | Tractable Variational Structures for Approximating Graphical Models | 1998 | 23 |
About David Barber
David Barber is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Management Science and Operations Research, having authored 87 papers that have together received 2.8k indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (22 papers), Neural Networks and Applications (21 papers), Blind Source Separation Techniques (11 papers), Bayesian Methods and Mixture Models (10 papers), Bayesian Modeling and Causal Inference (10 papers), Machine Learning and Algorithms (7 papers), Target Tracking and Data Fusion in Sensor Networks (7 papers) and Music and Audio Processing (6 papers). The work is most often cited by research in Artificial Intelligence (1.5k citations), Signal Processing (412 citations), Computer Vision and Pattern Recognition (477 citations), Statistics and Probability (165 citations) and Statistics, Probability and Uncertainty (122 citations). David Barber has collaborated with scholars based in United Kingdom, Switzerland and Netherlands. Frequent co-authors include Christopher K. I. Williams, Felix Agakov, Chris Bishop, Ali Taylan Cemgil, Herwig Immervoll, Hilbert J. Kappen, Aleksandar Botev, Hippolyt Ritter, Peter Sollich and Silvia Chiappa. Their work appears in journals such as Neural Computation, Journal of Machine Learning Research, Europhysics Letters (EPL), IEEE Transactions on Audio Speech and Language Processing and IEEE Signal Processing 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.