What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Graphical model, Artificial intelligence, Belief propagation, Machine learning and Data mining are his primary areas of study.
His Graphical model research is multidisciplinary, incorporating elements of Particle filter, Message passing, Distributed computing and Statistical model.
His work deals with themes such as Satellite precipitation and Precipitation, which intersect with Artificial intelligence.
His Belief propagation study contributes to a more complete understanding of Algorithm.
His research investigates the link between Machine learning and topics such as Inference that cross with problems in Majority rule and Expectation–maximization algorithm.
His Data mining research is multidisciplinary, relying on both Poisson distribution and Count data.
His most cited work include:
- Nonparametric belief propagation for self-localization of sensor networks (480 citations)
- Fast collapsed gibbs sampling for latent dirichlet allocation (458 citations)
- Nonparametric belief propagation (409 citations)
What are the main themes of his work throughout his whole career to date?
Alexander T. Ihler mainly investigates Graphical model, Artificial intelligence, Algorithm, Belief propagation and Machine learning.
His Graphical model study incorporates themes from Theoretical computer science, Inference, Approximate inference, Message passing and Mathematical optimization.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Nonparametric statistics, Data mining and Pattern recognition.
Alexander T. Ihler has included themes like Sampling, Markov chain and Maximum a posteriori estimation in his Algorithm study.
His Belief propagation study combines topics in areas such as Fault, Particle filter, Random variable and Applied mathematics.
His study in the field of Structured prediction is also linked to topics like Crowdsourcing and Hidden variable theory.
He most often published in these fields:
- Graphical model (33.57%)
- Artificial intelligence (33.57%)
- Algorithm (30.07%)
What were the highlights of his more recent work (between 2017-2020)?
- Bounding overwatch (6.99%)
- Artificial intelligence (33.57%)
- Algorithm (30.07%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Bounding overwatch, Artificial intelligence, Algorithm, Graphical model and Task.
His studies deal with areas such as Theoretical computer science, Influence diagram and Expected utility hypothesis as well as Bounding overwatch.
In his study, Robustness is strongly linked to Machine learning, which falls under the umbrella field of Artificial intelligence.
His work in the fields of Algorithm, such as Tree, overlaps with other areas such as Node.
His Graphical model study frequently draws parallels with other fields, such as Inference.
His study in Approximate inference is interdisciplinary in nature, drawing from both Class, Monte Carlo method, Message passing and Markov chain.
Between 2017 and 2020, his most popular works were:
- A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information (19 citations)
- Estimating Warehouse Rental Price using Machine Learning Techniques (9 citations)
- Accelerating Dynamic Programs via Nested Benders Decomposition with Application to Multi-Person Pose Estimation (7 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His main research concerns Mathematical optimization, Artificial neural network, Artificial intelligence, Bounding overwatch and Precipitation.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Graphical model, Monte Carlo method and Inference.
The Artificial neural network study combines topics in areas such as Term, Deep learning and Real-time computing.
His study in the fields of Convolutional neural network and Hidden Markov model under the domain of Artificial intelligence overlaps with other disciplines such as Warehouse, Real estate and Renting.
He has included themes like Satellite, Remote sensing and Stage in his Precipitation study.
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