图 书 馆开课单位--斯坦福大学
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191CourseRank: A Closed-Community Social System through the Magnifying Glass[课程等级：放大镜下的封闭社区社会系统 ]
Koutrika Georgia(斯坦福大学) Social sites are extremely popular among users but user interactions in most sites revolve around relatively simple tasks, such as uploading resources...
热度：63
Koutrika Georgia(斯坦福大学) Social sites are extremely popular among users but user interactions in most sites revolve around relatively simple tasks, such as uploading resources...
热度：63
192Large-Scale Deep Unsupervised Learning Using Graphics Processors[使用图形处理器进行大规模深度无监督学习]
Rajat Raina(斯坦福大学) The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models w...
热度：26
Rajat Raina(斯坦福大学) The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models w...
热度：26
193Unsupervised Discovery of Structure, Succinct Representations and Sparsity[结构的无监督发现、简洁的表示和稀疏性]
Andrew Ng(斯坦福大学) We describe a class of unsupervised learning methods that learn sparse representations of the training data, and thereby identify useful features. Fur...
热度：38
Andrew Ng(斯坦福大学) We describe a class of unsupervised learning methods that learn sparse representations of the training data, and thereby identify useful features. Fur...
热度：38
194Reinforcement Learning, Apprenticeship Learning and Robotic Control[强化学习，学徒学习和机器人控制]
Andrew Ng(斯坦福大学) Reinforcement learning has proved to be a powerful method for robotic control. In this talk, drawing on examples from autonomous helicopter flight, qu...
热度：55
Andrew Ng(斯坦福大学) Reinforcement learning has proved to be a powerful method for robotic control. In this talk, drawing on examples from autonomous helicopter flight, qu...
热度：55
195Statistical Leverage and Improved Matrix Algorithms[统计杠杆和改进的矩阵算法]
Michael Mahoney(斯坦福大学) Given an m x n matrix A and a rank parameter k, define the leverage of the i-th row of A to be the i-th diagonal element of the projection matrix onto...
热度：89
Michael Mahoney(斯坦福大学) Given an m x n matrix A and a rank parameter k, define the leverage of the i-th row of A to be the i-th diagonal element of the projection matrix onto...
热度：89
196Modeling Social and Information Networks: Opportunities for Machine Learning[社交和信息网络建模：机器学习的机会]
Jure Leskovec(斯坦福大学) Emergence of the web, social media and online social networking websites gave rise to detailed traces of human social activity. This offers many oppor...
热度：31
Jure Leskovec(斯坦福大学) Emergence of the web, social media and online social networking websites gave rise to detailed traces of human social activity. This offers many oppor...
热度：31
197STAIR: The STanford Artificial Intelligence Robot project[STAIR：STanford人工智能机器人项目]
Andrew Ng, Andrew McCallum(斯坦福大学) This talk will describe the STAIR home assistant robot project, and several satellite projects that led to key STAIR components such as (i) robotic gr...
热度：84
Andrew Ng, Andrew McCallum(斯坦福大学) This talk will describe the STAIR home assistant robot project, and several satellite projects that led to key STAIR components such as (i) robotic gr...
热度：84
198Fast Gaussian Process Methods for Point Process Intensity Estimation[点过程强度估计的快速高斯处理方法]
John Cunningham(斯坦福大学) Point processes are difficult to analyze because they provide only a sparse and noisy observation of the intensity function driving the process. Gauss...
热度：64
John Cunningham(斯坦福大学) Point processes are difficult to analyze because they provide only a sparse and noisy observation of the intensity function driving the process. Gauss...
热度：64
199Scalable Modeling of Real Graphs using Kronecker Multiplication[基于Kronecker乘法的实图可扩展建模]
Jure Leskovec(斯坦福大学) Given a large, real graph, how can we generate a synthetic graph that matches its properties, i.e., it has similar degree distribution, similar (small...
热度：57
Jure Leskovec(斯坦福大学) Given a large, real graph, how can we generate a synthetic graph that matches its properties, i.e., it has similar degree distribution, similar (small...
热度：57
200Classifying single trial fMRI: What can machine learning learn?[单次试验功能磁共振的分类：机器学习能学到什么？]
Paul Mazaika(斯坦福大学) We describe three experiments combining neuroimaging and machine learning. The first experiment compares the performance of maximum likelihood and ne...
热度：88
Paul Mazaika(斯坦福大学) We describe three experiments combining neuroimaging and machine learning. The first experiment compares the performance of maximum likelihood and ne...
热度：88