图 书 馆开课单位--伦敦大学学院
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121Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search[使用基于样本的搜索进行高效的贝叶斯自适应强化学习]
Arthur Guez(伦敦大学学院) Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off explora...
热度：38
Arthur Guez(伦敦大学学院) Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off explora...
热度：38
122Probabilistic Integration for Uncertainty Quantification in Differential Equation Models[微分方程模型不确定性量化的概率积分法 ]
Ben Calderhead(伦敦大学学院) In this talk I discuss recent joint work with Oksana Chkrebtii, Prof. Dave Campbell and Prof. Mark Girolami, in which we develop a probabilistic forma...
热度：69
Ben Calderhead(伦敦大学学院) In this talk I discuss recent joint work with Oksana Chkrebtii, Prof. Dave Campbell and Prof. Mark Girolami, in which we develop a probabilistic forma...
热度：69
123Various Formulations for Learning the Kernel and Structured Sparsity[学习内核和结构稀疏性的各种公式 ]
Massimiliano Pontil(伦敦大学学院) I will review an approach to learning the kernel, which consists in minimizing a convex objective function over a prescribed set of kernel matrices. I...
热度：37
Massimiliano Pontil(伦敦大学学院) I will review an approach to learning the kernel, which consists in minimizing a convex objective function over a prescribed set of kernel matrices. I...
热度：37
124Multiple Gaussian Process Models[多高斯过程模型]
Cedric Archambeau(伦敦大学学院) We consider a Gaussian process formulation of the multiple kernel learning problem. The goal is to select the convex combination of kernel matrices th...
热度：98
Cedric Archambeau(伦敦大学学院) We consider a Gaussian process formulation of the multiple kernel learning problem. The goal is to select the convex combination of kernel matrices th...
热度：98
125Spectral learning of linear dynamics from generalisedlinear observations with application to neural population data[线性动力学的谱学从广义线性观测到应用于神经种群数据]
Lars Buesing(伦敦大学学院) Latent linear dynamical systems with generalised-linear observation models arise in a variety of applications, for example when modelling the spiking ...
热度：53
Lars Buesing(伦敦大学学院) Latent linear dynamical systems with generalised-linear observation models arise in a variety of applications, for example when modelling the spiking ...
热度：53
126A Family of Penalty Functions for Structured Sparsity[一组用于构造稀疏性的惩罚函数]
Jean Morales(伦敦大学学院) We study the problem of learning a sparse linear regression vector under additional conditions on the structure of its sparsity pattern. We present a ...
热度：81
Jean Morales(伦敦大学学院) We study the problem of learning a sparse linear regression vector under additional conditions on the structure of its sparsity pattern. We present a ...
热度：81
127Measures of Statistical Dependence[统计依赖度量]
Arthur Gretton(伦敦大学学院) A number of important problems in signal processing depend on measures of statistical dependence. For instance, this dependence is minimised in the co...
热度：118
Arthur Gretton(伦敦大学学院) A number of important problems in signal processing depend on measures of statistical dependence. For instance, this dependence is minimised in the co...
热度：118
128What Harm Does Pathological Synchronization in Parkinson's Disease Do?[帕金森病的病理同步有什么危害？]
Peter Brown(伦敦大学学院) Like tuning in a station on the FM band of a radio, neuroscientists can detect the particular frequencies of our brains in action. And just as on the ...
热度：60
Peter Brown(伦敦大学学院) Like tuning in a station on the FM band of a radio, neuroscientists can detect the particular frequencies of our brains in action. And just as on the ...
热度：60
129Online Similarity Prediction of Networked Data from Known and Unknown Graphs[已知图和未知图的网络数据的在线相似度预测]
Mark Herbster(伦敦大学学院) We consider online similarity prediction problems over networked data. We begin by relating this task to the more standard class prediction problem, s...
热度：29
Mark Herbster(伦敦大学学院) We consider online similarity prediction problems over networked data. We begin by relating this task to the more standard class prediction problem, s...
热度：29
130Modeling Natural Sounds with Modulation Cascade Processes[用调制级联过程建模自然声音]
Richard Turner(伦敦大学学院) Auditory scene analysis is extremely challenging. One approach, perhaps that adopted by the brain, is to shape useful representations of sounds on pri...
热度：88
Richard Turner(伦敦大学学院) Auditory scene analysis is extremely challenging. One approach, perhaps that adopted by the brain, is to shape useful representations of sounds on pri...
热度：88