图 书 馆开课单位--澳大利亚信息通信技术研究中心
1
2
3
4
5
6
7
8
9
10
1Anti-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：85
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：85
2Consistent 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：51
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：51
3Introduction 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：93
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：93
45Learning 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：37
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：37
6Inference 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：98
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：98
7Reinforcement 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：97
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：97
8Bioinformatics 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：60
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：60
9Graphical 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：47
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：47
10Artificial 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：73
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：73