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- Hilbert J. Kappen

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Engineering and Technology
D-index
32
Citations
4,695
166
World Ranking
6816
National Ranking
130

- Artificial intelligence
- Statistics
- Machine learning

His primary areas of investigation include Artificial intelligence, Stochastic control, Algorithm, Mathematical optimization and Belief propagation. His research on Artificial intelligence often connects related topics like Drone. His Stochastic control study is concerned with the field of Optimal control as a whole.

His work on Computational complexity theory as part of his general Algorithm study is frequently connected to SQL, thereby bridging the divide between different branches of science. His Mathematical optimization research incorporates themes from Control theory, Markov decision process, Benchmark, Monte Carlo method and Reinforcement learning. His Belief propagation study integrates concerns from other disciplines, such as Discrete mathematics, Graphical model, Fixed point and Inference.

- Linear theory for control of nonlinear stochastic systems. (252 citations)
- Optimal control as a graphical model inference problem (228 citations)
- Path integrals and symmetry breaking for optimal control theory (220 citations)

Artificial intelligence, Algorithm, Artificial neural network, Mathematical optimization and Applied mathematics are his primary areas of study. His research investigates the connection between Artificial intelligence and topics such as Data mining that intersect with issues in Bayesian probability. His Algorithm research is multidisciplinary, incorporating perspectives in Inference, Gradient descent, Graphical model, Boltzmann machine and Function.

His Artificial neural network research incorporates themes from Probability distribution, Attractor and Neuroscience, Information processing. His primary area of study in Mathematical optimization is in the field of Stochastic control. His study looks at the relationship between Applied mathematics and fields such as Belief propagation, as well as how they intersect with chemical problems.

- Artificial intelligence (25.60%)
- Algorithm (16.91%)
- Artificial neural network (16.43%)

- Mathematical optimization (14.49%)
- Path integral formulation (5.80%)
- Stochastic control (10.14%)

His primary areas of study are Mathematical optimization, Path integral formulation, Stochastic control, Algorithm and Artificial intelligence. His Mathematical optimization study also includes fields such as

- Limit, Reinforcement learning and Function most often made with reference to Representation,
- Smoothing, which have a strong connection to Stochastic process. In Stochastic control, he works on issues like Control theory, which are connected to Control, Optimal control and Importance sampling.

The Algorithm study combines topics in areas such as Gradient descent, Perceptron and Probability and statistics. His Artificial neural network and Benchmark study in the realm of Artificial intelligence connects with subjects such as Optical flow. In general Artificial neural network, his work in Random neural network is often linked to Basis linking many areas of study.

- Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone (76 citations)
- Learning Universal Computations with Spikes. (63 citations)
- Adaptive Importance Sampling for Control and Inference (54 citations)

- Artificial intelligence
- Statistics
- Machine learning

His main research concerns Mathematical optimization, Stochastic control, Algorithm, Control theory and Monte Carlo method. Hilbert J. Kappen performs multidisciplinary studies into Mathematical optimization and Path integral formulation in his work. His Algorithm research integrates issues from Gradient descent, Artificial neural network, Probability distribution and Smoothing.

His work deals with themes such as Control, Importance sampling and Optimal control, which intersect with Control theory. His studies deal with areas such as Probabilistic inference, State, Relation, Task and Computation as well as Optimal control. His studies examine the connections between Monte Carlo method and genetics, as well as such issues in Stochastic process, with regards to Posterior probability, Additive smoothing, Kernel and Filter.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Practical confidence and prediction intervals for prediction tasks

T. Heskes;W.A.J.J. Wiegerinck;H.J. Kappen.

Kappen, B. (ed.), Neural Networks: Best Practice in Europe : Proceedings of the Stichting Neurale Netwerken Conference 1997 (Progress in Neural Processing) **(1997)**

411 Citations

Path integrals and symmetry breaking for optimal control theory

H J Kappen.

Journal of Statistical Mechanics: Theory and Experiment **(2005)**

307 Citations

Linear theory for control of nonlinear stochastic systems.

Hilbert J. Kappen.

Physical Review Letters **(2005)**

307 Citations

Sufficient Conditions for Convergence of the Sum–Product Algorithm

J.M. Mooij;H.J. Kappen.

IEEE Transactions on Information Theory **(2007)**

285 Citations

Optimal control as a graphical model inference problem

Hilbert J. Kappen;Vicenç Gómez;Manfred Opper.

Machine Learning **(2012)**

282 Citations

Efficient learning in Boltzmann machines using linear response theory

H. J. Kappen;F. B. Rodríguez.

Neural Computation **(1998)**

240 Citations

A generative model for music transcription

A.T. Cemgil;H.J. Kappen;D. Barber.

IEEE Transactions on Audio, Speech, and Language Processing **(2006)**

167 Citations

Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone

Kimberly McGuire;Guido de Croon;Christophe De Wagter;Karl Tuyls.

international conference on robotics and automation **(2017)**

154 Citations

Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model

Mohammad Gheshlaghi Azar;Rémi Munos;Hilbert J. Kappen.

Machine Learning **(2013)**

152 Citations

An introduction to stochastic control theory, path integrals and reinforcement learning

Hilbert J. Kappen.

COOPERATIVE BEHAVIOR IN NEURAL SYSTEMS: Ninth Granada Lectures **(2007)**

151 Citations

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