Boltzmann machine, as a fundamental construction block of deep belief network and deep Boltzmann machines, is widely used in deep learning community and great success has been achieved. However, theoretical understanding of many aspects of it is still far from clear. In this paper, we studied the Rademacher complexity of both the asymptotic restricted Boltzmann machine and the practical implementation with single-step contrastive divergence (CD-1) procedure. Our results disclose the fact that practical implementation training procedure indeed increased the Rademacher complexity of restricted Boltzmann machines. A further research direction might be the investigation of the VC dimension of a compositional function used in the CD-1 procedure.

**Click to Read Paper and Get Code*** accepted for publication by Neural Networks

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Deep Distributed Random Samplings for Supervised Learning: An Alternative to Random Forests?

Jan 28, 2015

Xiao-Lei Zhang

In (\cite{zhang2014nonlinear,zhang2014nonlinear2}), we have viewed machine learning as a coding and dimensionality reduction problem, and further proposed a simple unsupervised dimensionality reduction method, entitled deep distributed random samplings (DDRS). In this paper, we further extend it to supervised learning incrementally. The key idea here is to incorporate label information into the coding process by reformulating that each center in DDRS has multiple output units indicating which class the center belongs to. The supervised learning method seems somewhat similar with random forests (\cite{breiman2001random}), here we emphasize their differences as follows. (i) Each layer of our method considers the relationship between part of the data points in training data with all training data points, while random forests focus on building each decision tree on only part of training data points independently. (ii) Our method builds gradually-narrowed network by sampling less and less data points, while random forests builds gradually-narrowed network by merging subclasses. (iii) Our method is trained more straightforward from bottom layer to top layer, while random forests build each tree from top layer to bottom layer by splitting. (iv) Our method encodes output targets implicitly in sparse codes, while random forests encode output targets by remembering the class attributes of the activated nodes. Therefore, our method is a simpler, more straightforward, and maybe a better alternative choice, though both methods use two very basic elements---randomization and nearest neighbor optimization---as the core. This preprint is used to protect the incremental idea from (\cite{zhang2014nonlinear,zhang2014nonlinear2}). Full empirical evaluation will be announced carefully later.
Jan 28, 2015

Xiao-Lei Zhang

* This paper has been withdrawn by the author. The idea is wrong and is no longer to be posed on site. The paper will no longer be updated

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Heuristic Ternary Error-Correcting Output Codes Via Weight Optimization and Layered Clustering-Based Approach

Apr 23, 2014

Xiao-Lei Zhang

Apr 23, 2014

Xiao-Lei Zhang

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Learning Deep Representation Without Parameter Inference for Nonlinear Dimensionality Reduction

Jan 02, 2014

Xiao-Lei Zhang

Unsupervised deep learning is one of the most powerful representation learning techniques. Restricted Boltzman machine, sparse coding, regularized auto-encoders, and convolutional neural networks are pioneering building blocks of deep learning. In this paper, we propose a new building block -- distributed random models. The proposed method is a special full implementation of the product of experts: (i) each expert owns multiple hidden units and different experts have different numbers of hidden units; (ii) the model of each expert is a k-center clustering, whose k-centers are only uniformly sampled examples, and whose output (i.e. the hidden units) is a sparse code that only the similarity values from a few nearest neighbors are reserved. The relationship between the pioneering building blocks, several notable research branches and the proposed method is analyzed. Experimental results show that the proposed deep model can learn better representations than deep belief networks and meanwhile can train a much larger network with much less time than deep belief networks.
Jan 02, 2014

Xiao-Lei Zhang

* This paper has been withdrawn by the author due to a lack of full empirical evaluation

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A Stochastic Composite Gradient Method with Incremental Variance Reduction

Jun 24, 2019

Junyu Zhang, Lin Xiao

Jun 24, 2019

Junyu Zhang, Lin Xiao

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Sentiment Tagging with Partial Labels using Modular Architectures

Jun 04, 2019

Xiao Zhang, Dan Goldwasser

Jun 04, 2019

Xiao Zhang, Dan Goldwasser

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PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval

Apr 22, 2019

Wenxiao Zhang, Chunxia Xiao

Apr 22, 2019

Wenxiao Zhang, Chunxia Xiao

* Accepted to CVPR 2019

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* 16 pages, 5 figures, 3 tables

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Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization

Sep 09, 2015

Yuchen Zhang, Lin Xiao

Sep 09, 2015

Yuchen Zhang, Lin Xiao

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Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss

Jan 01, 2015

Yuchen Zhang, Lin Xiao

Jan 01, 2015

Yuchen Zhang, Lin Xiao

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A Proximal Stochastic Gradient Method with Progressive Variance Reduction

Mar 19, 2014

Lin Xiao, Tong Zhang

Mar 19, 2014

Lin Xiao, Tong Zhang

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A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem

Mar 14, 2012

Lin Xiao, Tong Zhang

Mar 14, 2012

Lin Xiao, Tong Zhang

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