What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Pascal Poupart mostly deals with Artificial intelligence, Partially observable Markov decision process, Mathematical optimization, Markov decision process and Machine learning.
His research in Artificial intelligence focuses on subjects like Dementia, which are connected to Human–computer interaction.
His Partially observable Markov decision process research integrates issues from Range, Value and Set.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Space and Reinforcement learning.
His Markov decision process research is multidisciplinary, incorporating perspectives in Network management, Scalability, Markov chain and Bellman equation.
The study incorporates disciplines such as Testbed and Decision theory in addition to Machine learning.
His most cited work include:
- An analytic solution to discrete Bayesian reinforcement learning (271 citations)
- Point-Based Value Iteration for Continuous POMDPs (208 citations)
- Exploiting structure to efficiently solve large scale partially observable markov decision processes (196 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Mathematical optimization, Partially observable Markov decision process, Markov decision process and Machine learning.
Pascal Poupart interconnects Computer vision and Natural language processing in the investigation of issues within Artificial intelligence.
His research in the fields of Bellman equation and Optimization problem overlaps with other disciplines such as Observable, Control theory and Decision quality.
Pascal Poupart studied Partially observable Markov decision process and Inference that intersect with Semantics and Dynamic Bayesian network.
His biological study spans a wide range of topics, including Linear programming, Set, Markov model and Benchmark.
His Artificial neural network study integrates concerns from other disciplines, such as Algorithm and Representation.
He most often published in these fields:
- Artificial intelligence (40.30%)
- Mathematical optimization (28.36%)
- Partially observable Markov decision process (19.90%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (40.30%)
- Theoretical computer science (7.96%)
- Knowledge graph (2.49%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Theoretical computer science, Knowledge graph, Artificial neural network and Heuristics.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, State and Natural language processing.
His work in Machine learning addresses issues such as Probabilistic logic, which are connected to fields such as Cognitive science and Property.
Pascal Poupart combines subjects such as Representation, Feature, Markov decision process and Feature learning with his study of Theoretical computer science.
His study looks at the relationship between Artificial neural network and topics such as Algorithm, which overlap with Autoencoder, Generative model, Generative grammar and Monotonic function.
His work deals with themes such as Matching, Natural language, Bayesian probability, Moment and Boolean satisfiability problem, which intersect with Heuristics.
Between 2018 and 2021, his most popular works were:
- Diachronic Embedding for Temporal Knowledge Graph Completion (35 citations)
- Representation Learning for Dynamic Graphs: A Survey (25 citations)
- Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey. (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Theoretical computer science, Knowledge graph, Artificial intelligence, Graph and Natural language.
His Theoretical computer science research incorporates elements of Range, Feature, Representation and Feature learning.
His Feature learning study combines topics in areas such as Encoder, Recommender system, Perspective and Categorization.
His work carried out in the field of Recommender system brings together such families of science as Computational finance, Inference and Social network.
His Knowledge graph research incorporates themes from Embedding and Contrast.
The Artificial intelligence study combines topics in areas such as State and Relaxation.
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