图 书 馆开课单位--牛津大学
1
2
3
4
5
6
7
8
9
10
1Fisher Vector Faces in the Wild[野外费舍尔矢量面孔]
Karen Simonyan(牛津大学) Several recent papers on automatic face verification have significantly raised the performance bar by developing novel, specialised representations th...
热度：29
Karen Simonyan(牛津大学) Several recent papers on automatic face verification have significantly raised the performance bar by developing novel, specialised representations th...
热度：29
2High throughput network analysis[高通量网络分析]
Sumeet Agarwal(牛津大学) Here, we presume that there is some valuable information encoded in the network; the problem is simply to find it. One approach for doing so is to dra...
热度：23
Sumeet Agarwal(牛津大学) Here, we presume that there is some valuable information encoded in the network; the problem is simply to find it. One approach for doing so is to dra...
热度：23
3Bayesian Optimization in a Billion Dimensions via Random Embeddings[基于随机嵌入的十亿维贝叶斯优化]
Nando de Freitas(牛津大学) Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user...
热度：62
Nando de Freitas(牛津大学) Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user...
热度：62
4Build it, and they will come: Applications of semantic technology[建立它，它们就会到来:语义技术的应用]
Ian Horrocks(牛津大学) Semantic technologies are rapidly becoming mainstream, with RDF, OWL and SPARQL now supported by a range of commercial systems and used in diverse app...
热度：32
Ian Horrocks(牛津大学) Semantic technologies are rapidly becoming mainstream, with RDF, OWL and SPARQL now supported by a range of commercial systems and used in diverse app...
热度：32
5PCA by Determinant Optimization has no Spurious Local Optima[基于行列式优化的PCA没有虚假局部最优]
Raphael Hauser(牛津大学) PCA by Determinant Optimization has no Spurious Local Optima
热度：32
Raphael Hauser(牛津大学) PCA by Determinant Optimization has no Spurious Local Optima
热度：32
6Trainable visual models for object classification[物体分类的可训练视觉模型]
Andrew Zisserman(牛津大学) The general theme of the tutorial will be 'trainable visual models for object classification'. I will cover: the difficulty of the problem a f...
热度：37
Andrew Zisserman(牛津大学) The general theme of the tutorial will be 'trainable visual models for object classification'. I will cover: the difficulty of the problem a f...
热度：37
7Query-Based Entity Comparison in Knowledge Graphs Revisited[知识图中基于查询的实体比较]
Alina Petrova(牛津大学) Query-Based Entity Comparison in Knowledge Graphs Revisited
热度：33
Alina Petrova(牛津大学) Query-Based Entity Comparison in Knowledge Graphs Revisited
热度：33
8SEO: A Scientific Events Data Mode[SEO：科学事件数据模式]
Sahar Vahdati(牛津大学) SEO: A Scientific Events Data Mode
热度：35
Sahar Vahdati(牛津大学) SEO: A Scientific Events Data Mode
热度：35
9Mondrian forests: Efficient random forests for streaming data via Bayesian nonparametrics[蒙德里安森林：通过贝叶斯非参数数据流的有效随机森林]
Yee Whye Teh(牛津大学) Ensembles of randomized decision trees are widely used for classification and regression tasks in machine learning and statistics. They achieve compet...
热度：162
Yee Whye Teh(牛津大学) Ensembles of randomized decision trees are widely used for classification and regression tasks in machine learning and statistics. They achieve compet...
热度：162
10Advanced STED microscopy of the membrane organisation in activating T-cells[激活T细胞的膜组织高级扫描电镜]
Iztok Urbančič(牛津大学) Nanoscale organization of the membranes of living cells plays crucial roles in numerous vital processes, including during the activation of T-cells an...
热度：63
Iztok Urbančič(牛津大学) Nanoscale organization of the membranes of living cells plays crucial roles in numerous vital processes, including during the activation of T-cells an...
热度：63