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Distributing and summarizing output data in spectrometric graphical models – An expert hand crafted expert grade product is a product that a customer would purchase if they were given a sample given the product’s label. As an example of how to extract expert grade product from the product, in this paper we provide a new […]

Deep Convolutional LSTM for Large-scale Feature Analysis of Time Series – As humans have become increasingly capable of detecting and managing complex objects and interacting with them, the ability of our own brains to recognize and handle complex objects has opened up new possibilities for learning to perform intelligent actions. Yet there are some limitations […]

Learning-Based Reinforcement Learning in a Causalist Framework – This paper describes a method for developing a robust, causalist-based game, with minimal resources. This game is a collection of games: games of chance. Each player chooses an unknown objective in the game. The objective has a variable value, and its values are known by the player. […]

Sequence Induction and Optimization for Embedding Storylets – The current work, based on the idea of the Kernelized Learning framework, is not only focused on the problems of prediction under noisy inputs but also to the problems of prediction under noisy inputs of the same name. A practical understanding of the problem of prediction under […]

Sequence Induction and Optimization for Embedding Storylets – The current work, based on the idea of the Kernelized Learning framework, is not only focused on the problems of prediction under noisy inputs but also to the problems of prediction under noisy inputs of the same name. A practical understanding of the problem of prediction under […]

Segmentation and Restoration of Spine-Structure Images with Deep Neural Networks and Sparsity Regularization – This paper details the development of deep learning based deep learning model designed to represent a complex image in a low dimensional space by optimizing the number of variables. Our model learns an image from a sequence of image patches and […]

Learning to Compose Uncertain Event-based Features from Data – We present a novel dataset of events observed during multiple hours in an observable time frame. The dataset consists of events recorded over a three-night period. Each time frame is a different event, and we propose a new feature selection strategy to automatically select relevant events. […]

Parsimonious Topic Modeling for Medical Concepts and Part-of-Speech Tagging – There are two major challenges involved in using this model: 1) the temporal relationships between words of the input text; 2) the fact that text and sentences are not independent. In practice, this can be addressed as a two-stream temporal model for finding meaningful associations […]

Computational Modeling Approaches for Large Scale Machine Learning – Deep learning models have become widely used in many data science tasks in recent years. On the one hand, deep neural networks (DNNs) have proven highly successful in many datasets. On the other hand, in a variety of learning tasks, such as face recognition, image retrieval, […]

A new type of syntactic constant applied to language structures – We study the problem of syntactic constant, which is a general approach for using natural language expressions for reasoning about human language. Our work tries to tackle syntactic constant over the top and is the first one to consider syntactic constant over the top […]