图 书 馆开课单位--多伦多大学
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31Polynomial Shape from Shading[从明暗恢复形状的多项式]
Ady Ecker(多伦多大学) We examine the shape from shading problem without boundary conditions as a polynomial system. This view allows, in generic cases, a complete solution ...
热度：32
Ady Ecker(多伦多大学) We examine the shape from shading problem without boundary conditions as a polynomial system. This view allows, in generic cases, a complete solution ...
热度：32
32Practical Variational Inference for Neural Networks[神经网络的实际变分推理]
Alex Graves(多伦多大学) Variational methods have been previously explored as a tractable approximation to Bayesian inference for neural networks. However the approaches propo...
热度：68
Alex Graves(多伦多大学) Variational methods have been previously explored as a tractable approximation to Bayesian inference for neural networks. However the approaches propo...
热度：68
33Tree-Structured Stick Breaking for Hierarchical Data[分层数据的树形结构分断]
Ryan Prescott Adams(多伦多大学) Many data are naturally modeled by an unobserved hierarchical structure. In this paper we propose a flexible nonparametric prior over unknown data hie...
热度：55
Ryan Prescott Adams(多伦多大学) Many data are naturally modeled by an unobserved hierarchical structure. In this paper we propose a flexible nonparametric prior over unknown data hie...
热度：55
34Learning to Learn with Compound HD Models[学习与复合高清模型]
Russ R Salakhutdinov(多伦多大学) We introduce HD (or "Hierarchical-Deep") models, a new compositional learning architecture that integrates deep learning models with structu...
热度：40
Russ R Salakhutdinov(多伦多大学) We introduce HD (or "Hierarchical-Deep") models, a new compositional learning architecture that integrates deep learning models with structu...
热度：40
35Measuring Semantic Relatedness Across Languages[跨语言的语义相关性度量]
Alistair Kennedy(多伦多大学) Measures of Semantic Relatedness are well established in Natural Language Processing. Their purpose is to determine the degree of relatedness between ...
热度：60
Alistair Kennedy(多伦多大学) Measures of Semantic Relatedness are well established in Natural Language Processing. Their purpose is to determine the degree of relatedness between ...
热度：60
36ImageNet Classification with Deep Convolutional Neural Networks[深度卷积神经网络在ImageNet中的医学分级]
Alex Krizhevsky(多伦多大学) We trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into ...
热度：65
Alex Krizhevsky(多伦多大学) We trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into ...
热度：65
3738Uncorrelated Multilinear Principal Component Analysis through Successive Variance Maximization[不相关的多线性主成分分析通过连续的方差最大化]
Haiping Lu(多伦多大学) Tensorial data are frequently encountered in various machine learning tasks today and dimensionality reduction is one of their most important applicat...
热度：126
Haiping Lu(多伦多大学) Tensorial data are frequently encountered in various machine learning tasks today and dimensionality reduction is one of their most important applicat...
热度：126
39403D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model[三维的目标检测与可变形的三维长方体模型的观点估计]
Sanja Fidler(多伦多大学) This paper addresses the problem of category-level 3D object detection. Given a monocular image, our aim is to localize the objects in 3D by enclosing...
热度：79
Sanja Fidler(多伦多大学) This paper addresses the problem of category-level 3D object detection. Given a monocular image, our aim is to localize the objects in 3D by enclosing...
热度：79