What is he best known for?
The fields of study he is best known for:
- 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.
His most cited work include:
- 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)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (25.60%)
- Algorithm (16.91%)
- Artificial neural network (16.43%)
What were the highlights of his more recent work (between 2014-2021)?
- Mathematical optimization (14.49%)
- Path integral formulation (5.80%)
- Stochastic control (10.14%)
In recent papers he was focusing on the following fields of study:
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.
Between 2014 and 2021, his most popular works were:
- 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)
In his most recent research, the most cited papers focused on:
- 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.
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