图 书 馆开课单位--多伦多大学
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51BoltzRank: Learning to Maximize Expected Ranking Gain[BoltzRank：学习最大化预期排名增益]
Maksims Volkovs(多伦多大学) Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn rankin...
热度：140
Maksims Volkovs(多伦多大学) Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn rankin...
热度：140
52Non-Linear Matrix Factorization with Gaussian Processes[高斯过程的非线性矩阵分解]
Raquel Urtasun(多伦多大学) A popular approach to collaborative filtering is matrix factorization. In this paper we consider the "probabilistic matrix factorization" an...
热度：111
Raquel Urtasun(多伦多大学) A popular approach to collaborative filtering is matrix factorization. In this paper we consider the "probabilistic matrix factorization" an...
热度：111
53A Simpler Unified Analysis of Budget Perceptrons[预算感知的一个更简单的统一分析]
Ilya Sutskever(多伦多大学) The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, whic...
热度：38
Ilya Sutskever(多伦多大学) The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, whic...
热度：38
54Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensities[具有高斯过程强度的泊松过程中的可跟踪非参数贝叶斯推断]
Ryan Prescott Adams(多伦多大学) The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian ...
热度：99
Ryan Prescott Adams(多伦多大学) The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian ...
热度：99
55Archipelago: Nonparametric Bayesian Semi-Supervised Learning[Archipelago：非参数贝叶斯半监督学习]
Ryan Prescott Adams(多伦多大学) Semi-supervised learning (SSL), is classification where additional unlabeled data can be used to improve accuracy. Generative approaches are appealing...
热度：97
Ryan Prescott Adams(多伦多大学) Semi-supervised learning (SSL), is classification where additional unlabeled data can be used to improve accuracy. Generative approaches are appealing...
热度：97
56Topologically-Constrained Latent Variable Models[拓扑约束潜变量模型]
Raquel Urtasun(多伦多大学) In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data sets this is inappropriate....
热度：62
Raquel Urtasun(多伦多大学) In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data sets this is inappropriate....
热度：62
57Training Restricted Boltzmann Machines using Approximations to the Likelihood Gradient[训练受限玻尔兹曼机器使用逼近似然梯度的近似]
Tijmen Tieleman(多伦多大学) A new algorithm for training Restricted Boltzmann Machines is introduced. The algorithm, named Persistent Contrastive Divergence, is different from th...
热度：69
Tijmen Tieleman(多伦多大学) A new algorithm for training Restricted Boltzmann Machines is introduced. The algorithm, named Persistent Contrastive Divergence, is different from th...
热度：69
58Nonparametric Bayesian Density Modeling with Gaussian Processes[高斯过程的非参数贝叶斯密度建模]
Ryan Prescott Adams(多伦多大学) We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Sample...
热度：111
Ryan Prescott Adams(多伦多大学) We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Sample...
热度：111
59Three New Graphical Models for Statistical Language Modelling[统计语言建模的三种新图形模型]
Andriy Mnih(多伦多大学) The supremacy of n-gram models in statistical language modelling has recently been challenged by parametric models that use distributed representation...
热度：116
Andriy Mnih(多伦多大学) The supremacy of n-gram models in statistical language modelling has recently been challenged by parametric models that use distributed representation...
热度：116
60Kernel-Based Copula Processes[基于内核的Copula流程]
Eddie K. H. Ng(多伦多大学) Kernel-based Copula Processes (KCPs), a new versatile tool for analyzing multiple time-series, are proposed here as a unifying framework to model the ...
热度：93
Eddie K. H. Ng(多伦多大学) Kernel-based Copula Processes (KCPs), a new versatile tool for analyzing multiple time-series, are proposed here as a unifying framework to model the ...
热度：93