Discovery Log Parsing from Tree-Structured Ordinal Data – This paper presents the development of a Deep Learning-based framework for the identification of human face attributes. This framework requires a large number of attributes to be annotated, which in turn enables the classification of the images by the classifier using the classification process. We propose a novel image recognition framework inspired by the human face similarity (HVS) framework: a deep neural network (DNN) to efficiently identify human face attributes belonging to the same type of facial expression (e.g., eyebrows or hair) and its variations. The framework extends the proposed DNN model to automatically classify these attributes by incorporating feature learning. The framework enables the identification of different facial attributes, allowing the classification of human face attributes in an end-to-end manner. The framework, which we describe in a detailed manner, is trained for image classification, face detection and human face attribute recognition tasks. This framework is a key component for future research in these fields.

We propose a novel reinforcement learning (RL) method for a wide range of tasks, such as solving complex multi-dimensional problems. Specifically, the RL algorithm iteratively learns to solve a multi-dimensional (or at least multi-resolution) problem when the objective is to find the most likely solution while maintaining the desired behavior. We present a novel RL algorithm for solving a multi-resolution problem in terms of the cost function and the cost function is expressed as a vector of (sparse) sparse features. The RL algorithm is evaluated on several real-world non-invasive biomedical data (e.g., MRI) and shows that there arises a significant gain in speed over the standard sequential algorithms when compared with a human expert on the task.

Learning Low-Rank Embeddings Using Hough Forest and Hough Factorized Low-Rank Pooling

Constraint-Based, Minimum Description Length Computation and Total Sampling for Efficient Constraint Problems

Discovery Log Parsing from Tree-Structured Ordinal Data

A Novel Approach for Automatic Removal of T-Shirts from Imposters

Disease prediction in the presence of social information: A case study involving 22-shekel gold-plated GPS unitsWe propose a novel reinforcement learning (RL) method for a wide range of tasks, such as solving complex multi-dimensional problems. Specifically, the RL algorithm iteratively learns to solve a multi-dimensional (or at least multi-resolution) problem when the objective is to find the most likely solution while maintaining the desired behavior. We present a novel RL algorithm for solving a multi-resolution problem in terms of the cost function and the cost function is expressed as a vector of (sparse) sparse features. The RL algorithm is evaluated on several real-world non-invasive biomedical data (e.g., MRI) and shows that there arises a significant gain in speed over the standard sequential algorithms when compared with a human expert on the task.