开课单位--澳大利亚信息通信技术研究中心
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Anti-Learning[反学习]
Adam Kowalczyk(澳大利亚信息通信技术研究中心) The Biological domain poses new challenges for statistical learning. In the talk we shall analyze and theoretically explain some counter-intuitive exp...
热度:74
Adam Kowalczyk(澳大利亚信息通信技术研究中心) The Biological domain poses new challenges for statistical learning. In the talk we shall analyze and theoretically explain some counter-intuitive exp...
热度:74
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Consistent Structured Estimation for Weighted Bipartite Matching[加权双向匹配的一致结构化估计]
Julian McAuley; James Petterson; Tibério Caetano(澳大利亚信息通信技术研究中心) Given a weighted bipartite graph, the assignment problem consists of finding the heaviest perfect match. This is a classical problem in combinatorial ...
热度:45
Julian McAuley; James Petterson; Tibério Caetano(澳大利亚信息通信技术研究中心) Given a weighted bipartite graph, the assignment problem consists of finding the heaviest perfect match. This is a classical problem in combinatorial ...
热度:45
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Introduction to Planning Domain Modeling in RDDL[RDDL中规划领域建模简介]
Scott Sanner(澳大利亚信息通信技术研究中心) RDDL is the Relational Dynamic Influence Diagram Language, the domain modeling language used in the ICAPS 2011 International Probabilistic Planning Co...
热度:75
Scott Sanner(澳大利亚信息通信技术研究中心) RDDL is the Relational Dynamic Influence Diagram Language, the domain modeling language used in the ICAPS 2011 International Probabilistic Planning Co...
热度:75
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Learning Convex Inference of Marginals[学习主要凸推理 ]
Justin Domke(澳大利亚信息通信技术研究中心) Graphical models trained using maximum likelihood are a common tool for probabilistic inference of marginal distributions. However, this approach suff...
热度:29
Justin Domke(澳大利亚信息通信技术研究中心) Graphical models trained using maximum likelihood are a common tool for probabilistic inference of marginal distributions. However, this approach suff...
热度:29
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Inference in Graphical Models[图形模型中的推理]
Tibério Caetano(澳大利亚信息通信技术研究中心) This short course will cover the basics of inference in graphical models. It will start by explaining the theory of probabilistic graphical models, in...
热度:90
Tibério Caetano(澳大利亚信息通信技术研究中心) This short course will cover the basics of inference in graphical models. It will start by explaining the theory of probabilistic graphical models, in...
热度:90
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Reinforcement Learning[强化学习]
Douglas Aberdeen(澳大利亚信息通信技术研究中心) Reinforcement learning is about learning good control policies given only weak performance feedback: occasional scalar rewards that might be delayed f...
热度:85
Douglas Aberdeen(澳大利亚信息通信技术研究中心) Reinforcement learning is about learning good control policies given only weak performance feedback: occasional scalar rewards that might be delayed f...
热度:85
![](functions/showpic.php?filename=2016070502272710.png)
Bioinformatics Challenge: Learning in Very High Dimensions with Very Few Samples[生物信息学挑战:在非常高的尺寸与非常少的样本中学习]
Adam Kowalczyk(澳大利亚信息通信技术研究中心) Dedicated machine learning procedures have already become an integral part of modern genomics and proteomics. However, these very high dimensional and...
热度:54
Adam Kowalczyk(澳大利亚信息通信技术研究中心) Dedicated machine learning procedures have already become an integral part of modern genomics and proteomics. However, these very high dimensional and...
热度:54
![](functions/showpic.php?filename=2016051708140656.jpg)
Graphical models[图形模型]
Tibério Caetano(澳大利亚信息通信技术研究中心) This course covers the basics of Probabilistic Graphical Models, including the basic theory of Bayesian Networks and Markov Random Fields, as well as ...
热度:42
Tibério Caetano(澳大利亚信息通信技术研究中心) This course covers the basics of Probabilistic Graphical Models, including the basic theory of Bayesian Networks and Markov Random Fields, as well as ...
热度:42
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Artificial Intelligence Planning[人工智能规划]
Jussi Rintanen(澳大利亚信息通信技术研究中心) The course presents the most important approaches to state space traversal used in planning, including techniques based on propositional satisfiabilit...
热度:64
Jussi Rintanen(澳大利亚信息通信技术研究中心) The course presents the most important approaches to state space traversal used in planning, including techniques based on propositional satisfiabilit...
热度:64