
A Novel Approach for the Detection of Cyclism in Diabetes Drug Versions Using Bayesian Classifiers
A Novel Approach for the Detection of Cyclism in Diabetes Drug Versions Using Bayesian Classifiers – Nonlinear and continuous regression models use Bayesian classifiers to predict the distribution of a variable, i.e., the data distribution of a model, over time. In continuous regression systems, the data are sampled from a log distribution over a variable, […]

A Novel Concept Search Method Based on Multiset Word Embedding and CharacterLevel Synthesis
A Novel Concept Search Method Based on Multiset Word Embedding and CharacterLevel Synthesis – Semantic representation learning (SE) is a very promising branch of cognitive science that aims to create a formal model for all the semantic information of a language (i.e. it is represented by its semantic states), while using only a small fraction […]

Comparing the LearningModel Classroom Approach, ConstraintBased Approach, and Conceptual Space
Comparing the LearningModel Classroom Approach, ConstraintBased Approach, and Conceptual Space – The success of deep learning systems requires a careful consideration of the complex interplay between learning and computational learning. In this work, we propose an endtoend approach to the analysis of deep neural networks. In particular, we address the problem of finding a suitable […]

Convolutional Kernel Machines
Convolutional Kernel Machines – Machine learning has become as powerful as computer vision in many aspects. We present an algorithm for unsupervised classification of images. Our method is based on kernelbased algorithms, and in particular is based on using the kernel of an unsupervised deep neural network (DNN). After training the DNN, the model learns […]

Recovery of Stochastic Vessels from Accelerating External Stimulation
Recovery of Stochastic Vessels from Accelerating External Stimulation – We study the problem of recovering and repairing small vessels of an unknown size. We present an initial solution using an iterative process to find the most likely position of vessel with the highest probability of success. We show that this process significantly reduces recovery time. […]

Stochastic Variational Inference with Batch and Weight Normalization
Stochastic Variational Inference with Batch and Weight Normalization – We study the computational complexity of Bayesian generative models and show that its convergence rate is close to a regularized value1 for an arbitrary dimension. This result applies to any supervised classification problem involving probability densities. We further show that if the parameter estimation model is […]

A MultiClass Kernel Classifier for Nonstationary Machine Learning
A MultiClass Kernel Classifier for Nonstationary Machine Learning – The problem of clustering of multiple data points in a distributed network is a realworld problem in many fields. It is easy to obtain goodquality metrics for the clustering process and to extract relevant information while keeping the data in the form of clusters. A number […]

Convolutional Neural Networks for Human Pose Estimation from Crowdsourcing Data
Convolutional Neural Networks for Human Pose Estimation from Crowdsourcing Data – We study the question of how to design an optimal learning model for a given set of inputs. Our goal is to address the problem of learning an optimal model for the input set, and to find a way to encode and embed the […]

Toward Fast, Nested Large Scale ZeroShot Learning for Video Recognition
Toward Fast, Nested Large Scale ZeroShot Learning for Video Recognition – We report about the challenge of a new problem which asks how to efficiently solve a sequence of sequential decision algorithms using twodimensional optimization methods. We show how to generalize the problem up to three times, allowing for solving more complex sequential decision problems […]

Multiclass SuperResolution with Conditional Generative Adversarial Networks
Multiclass SuperResolution with Conditional Generative Adversarial Networks – Neural networks have achieved great successes in many computer vision applications. In this paper, we develop a neural network model to solve image denoising problems. Our model consists of two components: a recurrent neural network component and a recurrent network component. The model is trained on the […]