开课单位--多伦多大学
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Polynomial 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:35
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:35
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Practical 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:70
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:70
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Tree-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:59
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:59
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Learning 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:42
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:42
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Measuring 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:61
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:61
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ImageNet 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
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Uncorrelated 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:128
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:128
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3D 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:81
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:81