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.
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.
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.
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.
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.
Nonparametric belief propagation
Erik B. Sudderth;Alexander T. Ihler;Michael Isard;William T. Freeman.
Communications of The ACM (2010)
Nonparametric belief propagation for self-localization of sensor networks
A.T. Ihler;J.W. Fisher;R.L. Moses;A.S. Willsky.
IEEE Journal on Selected Areas in Communications (2005)
Fast collapsed gibbs sampling for latent dirichlet allocation
Ian Porteous;David Newman;Alexander Ihler;Arthur Asuncion.
knowledge discovery and data mining (2008)
Nonparametric belief propagation
E.B. Sudderth;A.T. Ihler;W.T. Freeman;A.S. Willsky.
computer vision and pattern recognition (2003)
Loopy Belief Propagation: Convergence and Effects of Message Errors
Alexander T. Ihler;John W. Fischer;Alan S. Willsky.
Journal of Machine Learning Research (2005)
Variational Inference for Crowdsourcing
Qiang Liu;Jian Peng;Alex T Ihler.
neural information processing systems (2012)
Adaptive event detection with time-varying poisson processes
Alexander Ihler;Jon Hutchins;Padhraic Smyth.
knowledge discovery and data mining (2006)
Distributed fusion in sensor networks
M. Cetin;Lei Chen;J.W. Fisher;A.T. Ihler.
IEEE Signal Processing Magazine (2006)
Nonparametric belief propagation for self-calibration in sensor networks
Alexander T. Ihler;John W. Fisher;Randolph L. Moses;Alan S. Willsky.
information processing in sensor networks (2004)
Brain and muscle Arnt-like protein-1 (BMAL1) controls circadian cell proliferation and susceptibility to UVB-induced DNA damage in the epidermis
Mikhail Geyfman;Vivek Kumar;Qiang Liu;Rolando Ruiz.
Proceedings of the National Academy of Sciences of the United States of America (2012)
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