图 书 馆开课单位--爱丁堡大学
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21When Training and Test Distributions are Different: Characterising Learning Transfer[当训练和测试的分布是不同的：它的学习迁移]
Amos Storkey(爱丁堡大学) When Training and Test Distributions are Different: Characterising Learning Transfer.
热度：60
Amos Storkey(爱丁堡大学) When Training and Test Distributions are Different: Characterising Learning Transfer.
热度：60
2223Slice sampling covariance hyperparameters of latent[切片采样协方差参数的潜在]
Iain Murray(爱丁堡大学) The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model. In the Bayesian framework the co...
热度：39
Iain Murray(爱丁堡大学) The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model. In the Bayesian framework the co...
热度：39
2425Fast methods for sparse recovery: alternatives to L1[稀疏恢复的快速方法：L1的替代方法]
Mike Davies(爱丁堡大学) Finding sparse solutions to underdetermined inverse problems is a fundamental challenge encountered in a wide range of signal processing applications,...
热度：86
Mike Davies(爱丁堡大学) Finding sparse solutions to underdetermined inverse problems is a fundamental challenge encountered in a wide range of signal processing applications,...
热度：86
26Learning generative texture models with extended Fields-of-Experts[扩展专家领域的学习生成纹理模型]
Nicolas Heess(爱丁堡大学) 扩展专家领域的学习生成纹理模型
热度：35
Nicolas Heess(爱丁堡大学) 扩展专家领域的学习生成纹理模型
热度：35
272829Phrase-based and factored statistical machine translation[基于短语和因子的统计机器翻译]
Philipp Koehn(爱丁堡大学)
热度：33
Philipp Koehn(爱丁堡大学)
热度：33
30Label Propagation for Fine-Grained Cross-Lingual Genre Classification[细粒度跨语言体裁分类的标签传播 ]
Philipp Petrenz(爱丁堡大学 ) Cross-lingual methods can bring the benefits of genre classification to languages which lack genre-annotated training data. However, prior work in thi...
热度：66
Philipp Petrenz(爱丁堡大学 ) Cross-lingual methods can bring the benefits of genre classification to languages which lack genre-annotated training data. However, prior work in thi...
热度：66