
Structure Regular Languages
Structure Regular Languages – We propose a twolevel structureinvariantregular language model, the Regular Language Model (RNML). This model is trained with an external grammar. NMLMLs are similar to regular language models, but can be trained endtoend. The main innovation of NMLML is to be a recursive encoder of language. The encoder is a recursive encoder […]

Modeling and Analysis of NonUniform Graphical Models as Bayesian Models
Modeling and Analysis of NonUniform Graphical Models as Bayesian Models – The theory of natural selection has shown that a population of humans may be a unique type of agent, a model of its environment, and that it is capable of modeling a set of phenomena. However, it is unclear how, and how often, this […]

Learning the Structure of Data that are Discrete
Learning the Structure of Data that are Discrete – The development of a machine learning system that generalizes over a structured structure is a challenging problem for many researchers in machine learning. A common concern is that learning algorithms fail on the assumption that data represented by a structured structure is not necessarily a structured […]

A Generalized Spectral Unmixing Method for Dynamic Photo Regions Identification
A Generalized Spectral Unmixing Method for Dynamic Photo Regions Identification – We present a novel method for the generation of images under low light conditions on the basis of a convolutional neural network (CNN) based model. Specifically, we first train an unsupervised CNN for image generation. Then, we use this CNN to train a discriminator […]

Fast LowRank Matrix Estimation for HighDimensional Text Classification
Fast LowRank Matrix Estimation for HighDimensional Text Classification – Recently, many methods have been proposed to improve the precision of the semantic segmentation task. In this paper, two approaches are proposed to reduce the computational cost in semantic segmentation. First, a fast LSTM (Log2vec) classifier is employed by the algorithm that uses LSTMs as the […]

Unsupervised Active Learning with Partial Learning
Unsupervised Active Learning with Partial Learning – A method for automatically learning to perform intelligent actions from video by optimizing the modelfree training data of a given task is presented. Using a novel and fast learning algorithm, we show that a modified version of the KNNbased algorithm — KNet — learns to perform the task […]

Online Learning of Convex Sets
Online Learning of Convex Sets – Recent years have witnessed the emergence of a variety of research techniques to improve the understanding of the relationships among the different types of graphs for solving the complex optimization problems. However, these techniques often face the problem of defining a set of variables whose variables have complex forms […]

Learning Discriminative Models of Multichannel Nonlinear Dynamics
Learning Discriminative Models of Multichannel Nonlinear Dynamics – We present the application of a learningbased model called a generalised deep feedforward neural network (CNN), to the task of deep learning. We demonstrate its ability to extract information about different aspects of the world, such as the appearance of natural landscapes and its importance for humancomputer […]

A Robust Method for NonStationary Stochastic Regression
A Robust Method for NonStationary Stochastic Regression – Learning structured models requires an effective and efficient method to learn a model which is useful for modeling largescale data data. The purpose of this study is to design a robust method to model data with multiple dimensions. Given a data set and a large representation space, […]

Distributed Variational Modeling Strategies: A Generalization with Application to Automated Grading
Distributed Variational Modeling Strategies: A Generalization with Application to Automated Grading – This paper proposes a new deep reinforcement learning (DRM) approach for learning an algorithm for a particular distribution from data. The algorithm is simple and robust to outliers and random noises. The algorithm’s performance is assessed by calculating the marginal performance of the […]