David Barber
- Artificial Intelligence top 0.5%
- 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
- Signal Processing top 2%
- Blind Source Separation Techniques 11
- Speech and Audio Processing 6
- Statistics and Probability top 2%
- Co-authors
- Christopher K. I. WilliamsFelix AgakovChris BishopAli Taylan CemgilHerwig ImmervollHilbert J. KappenAleksandar BotevHippolyt Ritter
- Journals
- Neural Computation (3 papers)Journal of Machine Learning Research (3 papers)IEEE Signal Processing Letters (2 papers)
- Partner nations
- United KingdomSwitzerlandNetherlands
In The Last Decade
David Barber
82 papers receiving 2.6k citations
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-authorship network
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
| # | Work | ||
|---|---|---|---|
| 1 | Practical lossless compression with latent variables using bits back coding | 2019 | 6 |
| 2 | A Scalable Laplace Approximation for Neural Networks | 2018 | 51 |
| 3 | On Machine Learning and Programming Languages | 2018 | 6 |
| 4 | Auxiliary Variational MCMC. | 2018 | 1 |
| 5 | Thinking Fast and Slow with Deep Learning and Tree Search | 2017 | 23 |
| 6 | Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning | 2017 | 13 |
| 7 | Approximate Newton methods for policy search in Markov decision processes | 2016 | 6 |
| 8 | Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations | 2014 | 21 |
| 9 | A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes | 2012 | 9 |
| 10 | Affine Independent Variational Inference | 2012 | 7 |
| 11 | Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems | 2006 | 59 |
| 12 | 2006 | 82 | |
| 13 | Kernelized Infomax Clustering | 2005 | 17 |
| 14 | Dynamic Bayesian Networks with Deterministic Latent Tables | 2002 | 4 |
| 15 | Tractable Variational Structures for Approximating Graphical Models | 1998 | 23 |
| 16 | Radial Basis Functions: A Bayesian Treatment | 1997 | 9 |
| 17 | On-line Learning from Finite Training Sets in Nonlinear Networks | 1997 | 2 |
| 18 | Online Learning from Finite Training Sets: An Analytical Case Study | 1996 | 2 |
| 19 | Bayesian Model Comparison by Monte Carlo Chaining | 1996 | 5 |
| 20 | The practice of personnel management | 1970 | 1 |
About David Barber
David Barber is a scholar working on Artificial Intelligence, Signal Processing, Computational Mathematics, Computer Vision and Pattern Recognition and Statistics and Probability, 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 Speech 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, IEEE Signal Processing Letters, Europhysics Letters (EPL) and IEEE Transactions on Audio Speech and Language Processing.
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.