Learning Deep Transform Architectures using Label Class Discriminant Analysis – We present a new supervised learning framework for a novel problem: estimating the label space of natural images from a single, unlabeled unlabeled dataset of the same object within a given domain. While the supervised learning framework is widely applied to the recognition and labeling tasks, in this work we propose using a different classifier to automatically estimate the label space and find the right labels for the given domains. Experiments on the PASCAL VOC and CIFAR-10 datasets show that our framework provides significantly better results compared to the existing methods.

This paper discusses the problems of estimating, modeling and evaluating social network structure in social information. We propose a novel method for estimating the structure of networks with multiple hidden units. We construct a model, the top structure, and a latent variable by learning how this structure affects the information that is stored in the latent variables. The top structure is assumed to represent a continuous data set, that does not contain variables, a form of the continuous data that has no continuous data. We develop a new network model that captures the continuous structure in multiple hidden units. This model estimates both the structure with each hidden unit and the relationships between the hidden units. We present results on both synthetic and real data.

Theoretical Analysis of Chinese Word Embeddings’ Entailment Structure: Exploratory Approach

Fast and Accurate Low Rank Estimation Using Multi-resolution Pooling

Learning Deep Transform Architectures using Label Class Discriminant Analysis

Probabilistic Modeling of Time-Series for Spatio-Temporal Data with a Bayesian Network Adversary

Towards Better Analysis of Hierarchical Data Clustering with Applications to Topic ModelingThis paper discusses the problems of estimating, modeling and evaluating social network structure in social information. We propose a novel method for estimating the structure of networks with multiple hidden units. We construct a model, the top structure, and a latent variable by learning how this structure affects the information that is stored in the latent variables. The top structure is assumed to represent a continuous data set, that does not contain variables, a form of the continuous data that has no continuous data. We develop a new network model that captures the continuous structure in multiple hidden units. This model estimates both the structure with each hidden unit and the relationships between the hidden units. We present results on both synthetic and real data.