A General Framework of Learning Attribute Similarity in Deep Neural Networks – This paper discusses and refines the notion of a generic approach to the optimization of the gradient-based Gaussian process (GP) learning problem under a Gaussian distribution model. We have designed the GP to be a distribution model, which means that GP training can be done using either a priori or posterior knowledge about the distribution. We show how our algorithm can be directly extended to the GP problem from both the GP and posterior distributions, and propose an extension to the GP which reduces the optimization of the GP to the problem of choosing the optimal GP, rather than learning the GP to optimize the distribution model. From this point of view, we show how to perform the optimisation of the GP, and we discuss the potential application of our algorithm to optimization of GPs.

This paper addresses the problem of multi-dimensional Gaussian network (GAN) model generation from sparse data under some conditions. Recently, deep learning models have been proposed to learn state-of-the-art visual features from data sets or datasets, but the problem is still poorly understood. In this paper, we develop a novel deep learning architecture for feature generation from unsupervised sparse data, which outperforms state-of-the-art GAN methods by a large margin. We propose an efficient and generalization-free learning strategy that learns feature representations for both supervised and unsupervised data sets. We further improve this strategy by training the residual model on the data, which, in turn, provides a new discriminant analysis for the learned features. Experiments on the ImageNet dataset show that using our approach improves the performance of our unsupervised GAN model for several benchmark classification tasks, including image classification and text classification.

A Unified Deep Learning Framework for Multi-object Tracking

Robust Learning of Spatial Context-Dependent Kernels

A General Framework of Learning Attribute Similarity in Deep Neural Networks

Learning to recognize multiple handwritten attributes

Stochastic gradient descent using sparse regularizationThis paper addresses the problem of multi-dimensional Gaussian network (GAN) model generation from sparse data under some conditions. Recently, deep learning models have been proposed to learn state-of-the-art visual features from data sets or datasets, but the problem is still poorly understood. In this paper, we develop a novel deep learning architecture for feature generation from unsupervised sparse data, which outperforms state-of-the-art GAN methods by a large margin. We propose an efficient and generalization-free learning strategy that learns feature representations for both supervised and unsupervised data sets. We further improve this strategy by training the residual model on the data, which, in turn, provides a new discriminant analysis for the learned features. Experiments on the ImageNet dataset show that using our approach improves the performance of our unsupervised GAN model for several benchmark classification tasks, including image classification and text classification.