图 书 馆开课单位--阿尔伯塔大学
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11Verdi: Quality Estimation and Error Detection for Bilingual Corpora[Verdi：双语语料库的质量估计和错误检测]
Mingjun Zhao(阿尔伯塔大学) Verdi: Quality Estimation and Error Detection for Bilingual Corpora
热度：31
Mingjun Zhao(阿尔伯塔大学) Verdi: Quality Estimation and Error Detection for Bilingual Corpora
热度：31
12FDML: A Collaborative Machine Learning Framework for Distributed Features[FDML：一个面向分布式特征的协同机器学习框架]
Yaochen Hu(阿尔伯塔大学) Most current distributed machine learning systems try to scale up model training by using a data-parallel architecture that divides the computation fo...
热度：69
Yaochen Hu(阿尔伯塔大学) Most current distributed machine learning systems try to scale up model training by using a data-parallel architecture that divides the computation fo...
热度：69
13Direct Predictive Collaborative Control of a Prosthetic Arm[人工手臂的直接预测协同控制]
Patrick M. Pilarski(阿尔伯塔大学) We have developed an online learning system for the collaborative control of an assistive device. Collaborative control is a complex setting requiring...
热度：33
Patrick M. Pilarski(阿尔伯塔大学) We have developed an online learning system for the collaborative control of an assistive device. Collaborative control is a complex setting requiring...
热度：33
14Convex Two-Layer Modeling[凸双层造型]
Özlem Aslan(阿尔伯塔大学) Latent variable prediction models, such as multi-layer networks, impose auxiliary latent variables between inputs and outputs to allow automatic infer...
热度：29
Özlem Aslan(阿尔伯塔大学) Latent variable prediction models, such as multi-layer networks, impose auxiliary latent variables between inputs and outputs to allow automatic infer...
热度：29
15Learning About Sensorimotor Data[学习感觉运动的数据]
Sutton Richard S(阿尔伯塔大学) Temporal-difference (TD) learning of reward predictions underlies both reinforcement-learning algorithms and the standard dopamine model of reward-bas...
热度：31
Sutton Richard S(阿尔伯塔大学) Temporal-difference (TD) learning of reward predictions underlies both reinforcement-learning algorithms and the standard dopamine model of reward-bas...
热度：31
16Tradeoffs in online learning under partial information feedback[部分信息反馈下的在线学习中的权衡]
Csaba Szepesvári(阿尔伯塔大学) How should an online learner choose its actions to trade off between exploration and exploitation to maximize the accuracy of predictions where the ch...
热度：38
Csaba Szepesvári(阿尔伯塔大学) How should an online learner choose its actions to trade off between exploration and exploitation to maximize the accuracy of predictions where the ch...
热度：38
17Using upper confidence bounds to control exploration and exploitation[使用上的置信区间来控制勘探和开发]
Csaba Szepesvári(阿尔伯塔大学) 1.Bandit Problems – “Optimism in the Face Uncertainty” 2.Upper confidence based algorithms 3.Bandits in continuous time 4.B...
热度：59
Csaba Szepesvári(阿尔伯塔大学) 1.Bandit Problems – “Optimism in the Face Uncertainty” 2.Upper confidence based algorithms 3.Bandits in continuous time 4.B...
热度：59
18Video Competition Award Ceremony[视频竞赛颁奖典礼]
Aha David W;Michael Bowling(阿尔伯塔大学) As a sequel to the successful AAAI-07 and AAAI-08 AI Video Competitions, IJCAI now solicits submissions for the 3rd annual video competition! Its goal...
热度：47
Aha David W;Michael Bowling(阿尔伯塔大学) As a sequel to the successful AAAI-07 and AAAI-08 AI Video Competitions, IJCAI now solicits submissions for the 3rd annual video competition! Its goal...
热度：47
1920The use of Unlabeled Data in Supervised Learning: the Manifold Dossier[监督学习中未标记数据的使用：流形文件]
Csaba Szepesvári(阿尔伯塔大学) 监督学习中未标记数据的使用：流形文件
热度：36
Csaba Szepesvári(阿尔伯塔大学) 监督学习中未标记数据的使用：流形文件
热度：36