图 书 馆开课单位--蒙特利尔大学
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11Scaling Up Deep Learning[扩大深度学习]
Yoshua Bengio(蒙特利尔大学) Deep learning has rapidly moved from a marginal approach in the machine learning community less than ten years ago to one that has strong industrial i...
热度：44
Yoshua Bengio(蒙特利尔大学) Deep learning has rapidly moved from a marginal approach in the machine learning community less than ten years ago to one that has strong industrial i...
热度：44
12Extracting and Composing Robust Features with Denoising Autoencoders[具有去噪的自动编码器的提取与组合鲁棒特征]
Pascal Vincent(蒙特利尔大学) Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning...
热度：64
Pascal Vincent(蒙特利尔大学) Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning...
热度：64
13A Preliminary Evaluation of Word Representations for Named-Entity Recognition[命名实体识别的一个字表示的初步评价]
Joseph Turian(蒙特利尔大学) We use different word representations as word features for a named-entity recognition (NER) system with a linear model. This work is part of a larger ...
热度：39
Joseph Turian(蒙特利尔大学) We use different word representations as word features for a named-entity recognition (NER) system with a linear model. This work is part of a larger ...
热度：39
14Learning Deep Hierarchies of Representations[学习表述的深层次体系]
Yoshua Bengio;Samy Bengio(蒙特利尔大学) Whereas theoretical work suggests that deep architectures might be computationally and statistically more efficient at representing highly-varying fun...
热度：22
Yoshua Bengio;Samy Bengio(蒙特利尔大学) Whereas theoretical work suggests that deep architectures might be computationally and statistically more efficient at representing highly-varying fun...
热度：22
15Tutorial on Learning Deep Architectures[学习深层架构教程]
Yoshua Bengio; Yann LeCun(蒙特利尔大学) This short tutorial on deep learning will review a variety of methods for learning multi-level, hierarchical representations, emphasizing their common...
热度：35
Yoshua Bengio; Yann LeCun(蒙特利尔大学) This short tutorial on deep learning will review a variety of methods for learning multi-level, hierarchical representations, emphasizing their common...
热度：35
16The Neural Autoregressive Distribution Estimator, incl. discussion by Yoshua Bengio[神经自回归分布估计，以及Yoshua Bengio的相关讨论]
Yoshua Bengio;Hugo Larochelle(蒙特利尔大学) We describe a new approach for modeling the distribution of high-dimensional vectors of discrete variables. This model is inspired by the restricted B...
热度：92
Yoshua Bengio;Hugo Larochelle(蒙特利尔大学) We describe a new approach for modeling the distribution of high-dimensional vectors of discrete variables. This model is inspired by the restricted B...
热度：92
17Metropolis-Hastings Sampling in a FilterBoost Music Classifier[大都市黑斯廷斯采样在filterboost音乐分类]
Thierry Bertin-Mahieux(蒙特利尔大学)
热度：30
Thierry Bertin-Mahieux(蒙特利尔大学)
热度：30
18Manifold Tangent Classifier[流形切线分级机]
Yann Dauphin(蒙特利尔大学) We combine three important ideas present in previous work for building classifiers: the semi-supervised hypothesis (the input distribution contains in...
热度：30
Yann Dauphin(蒙特利尔大学) We combine three important ideas present in previous work for building classifiers: the semi-supervised hypothesis (the input distribution contains in...
热度：30
19Book-Adaptive and Book-Dependent Models to Accelerate Digitization of Early Music[书籍自适应和书籍依赖模型加速早期音乐的数字化]
Douglas Eck(蒙特利尔大学) Optical music recognition (OMR) enables early music collections to be digitized on a large scale. The workflow for such digitisation projects also inc...
热度：36
Douglas Eck(蒙特利尔大学) Optical music recognition (OMR) enables early music collections to be digitized on a large scale. The workflow for such digitisation projects also inc...
热度：36
20Tutorial on Learning Deep Architectures[学习深层架构的教程]
Yann LeCun, Yoshua Bengio(蒙特利尔大学) This short tutorial on deep learning will review a variety of methods for learning multi-level, hierarchical representations, emphasizing their common...
热度：29
Yann LeCun, Yoshua Bengio(蒙特利尔大学) This short tutorial on deep learning will review a variety of methods for learning multi-level, hierarchical representations, emphasizing their common...
热度：29