Learning sparse feature representations is a useful instrument for solving an
unsupervised learning problem. In this paper, we present three labeled
handwritten digit datasets, collectively called n-MNIST. Then, we propose a
novel framework for the classification of handwritten digits that learns sparse
representations using probabilistic quadtrees and Deep Belief Nets. On the
MNIST and n-MNIST datasets, our framework shows promising results and
significantly outperforms traditional Deep Belief Networks.

本文提出了一个新颖的基于概率四叉树和深信度网络的稀疏特征表示分类框架，用于手写数字的分类，在 MNIST 和 n-MNIST 数据集上，取得了有希望的结果，并显著优于传统的深信度网络。

使用概率四叉树和深度置信网络学习稀疏特征表示

Learning Sparse Feature Representations using Probabilistic Quadtrees  and Deep Belief Nets

Applying traditional collaborative filtering to digital publishing is
challenging because user data is very sparse due to the high volume of
documents relative to the number of users. Content based approaches, on the
other hand, is attractive because textual content is often very informative. In
this paper we describe large-scale content based collaborative filtering for
digital publishing. To solve the digital publishing recommender problem we
compare two approaches: latent Dirichlet allocation (LDA) and deep belief nets
(DBN) that both find low-dimensional latent representations for documents.
Efficient retrieval can be carried out in the latent representation. We work
both on public benchmarks and digital media content provided by Issuu, an
online publishing platform. This article also comes with a newly developed deep
belief nets toolbox for topic modeling tailored towards performance evaluation
of the DBN model and comparisons to the LDA model.

本文描述基于内容协同过滤的大规模数字出版物推荐问题的两种可能的解决方案：潜在狄利克雷分配（LDA）和深度置信网络（DBN），并对比它们在公共基准和来自 Issuu 的数字媒体内容上的表现。同时，该文章配有一个新开发的适用于 DBN 模型性能评估和 LDA 模型对比的主题建模工具箱。