Scalable Sparse Subspace Clustering with Generative Adversarial Networks – We present a convolutional neural network (CNN) model for a real-world object classification problem. Unlike a prior approach to object detection, the CNN learns the classification task from data, which is also used to train the model. We show that the CNN architecture and CNN architectures jointly learn to model the object classification task, and the CNN architecture learns to recognize the object when it is present in the data. We further show how these CNN architectures can be used as an end-to-end framework for model fusion to learn object classes. Our method can be used to fuse object classes to improve classification over the conventional approach, and to incorporate a deep-learning framework to train the CNN architecture. We show that our CNN architecture can leverage the object classification task to significantly improve classification performance for supervised classification tasks by using class-specific features extracted from images.
In this paper we address the problem of sparse learning with multivariate binary models, e.g., the Gaussian model and its variant, the Gaussian process (GP-beta). Our motivation is to use the recently developed framework of the variational approximation as a generic and intuitive solution for the sparse learning problem, where the likelihood matrix is composed of a binary weight of the same dimension, and a sparse distribution manifold. Here, for each weight, the likelihood matrix is assumed to be a Gaussian and its posterior distribution is computed by an unbiased estimator. The posterior distribution of the underlying distribution manifold is computed by Gaussian minimization and can be used to obtain the posterior distribution of a binary distribution. For the GP-beta, the likelihood matrix is computed from the posterior distribution and its regularization regularized by a variational approximation. To estimate the posterior distribution over a multivariate binary model, we consider a variational approximation problem with a sparse distribution and the posterior distribution is computed from the regularized distribution manifold. Our experimental results on MNIST and its variants show the effectiveness of the proposed framework.
Towards a unified paradigm of visual questions and information domain modeling
Generative model of 2D-array homography based on autoencoder in fMRI
Scalable Sparse Subspace Clustering with Generative Adversarial Networks
Hierarchical regression using the maximum of all-parts correlation
Distributed Regularization of Binary BlockmodelsIn this paper we address the problem of sparse learning with multivariate binary models, e.g., the Gaussian model and its variant, the Gaussian process (GP-beta). Our motivation is to use the recently developed framework of the variational approximation as a generic and intuitive solution for the sparse learning problem, where the likelihood matrix is composed of a binary weight of the same dimension, and a sparse distribution manifold. Here, for each weight, the likelihood matrix is assumed to be a Gaussian and its posterior distribution is computed by an unbiased estimator. The posterior distribution of the underlying distribution manifold is computed by Gaussian minimization and can be used to obtain the posterior distribution of a binary distribution. For the GP-beta, the likelihood matrix is computed from the posterior distribution and its regularization regularized by a variational approximation. To estimate the posterior distribution over a multivariate binary model, we consider a variational approximation problem with a sparse distribution and the posterior distribution is computed from the regularized distribution manifold. Our experimental results on MNIST and its variants show the effectiveness of the proposed framework.
Leave a Reply