
A Note on The Naive Bayes Method
A Note on The Naive Bayes Method – We study the practical problems of Bayesian inference in the Bayesian setting and a Bayesian inference methodology. A Bayesian inference framework is described and shown to outperform the stateoftheart baselines both in terms of accuracy and inference speed. The first task in the framework is to learn […]

Bayesian Information Extraction: A Survey
Bayesian Information Extraction: A Survey – Information extraction from synthetic data is a key challenge in medical imaging systems. In this article we describe a system that provides the opportunity to provide patientlevel information such as clinical notes as well as userlevel information about patient care. The system offers users a choice of their notes […]

Machine learning algorithms and RNNs with spatiotemporal consistency
Machine learning algorithms and RNNs with spatiotemporal consistency – We consider an objective function for a stochastic variable $f$, and propose a novel method, called stochasticlinearevolution, for solving it. Unlike existing stochastic linear equations, the $f$variables are generated in an unsupervised setting. We provide a theoretical justification for our approach, using the following terms: a) […]

Learning Deep Structured Models by Fully Convolutional Neural Networks Using Supervoxelbased Deep Learning
Learning Deep Structured Models by Fully Convolutional Neural Networks Using Supervoxelbased Deep Learning – Many computer vision tasks require large, dense data, with most approaches either using structured models or using linear models. In this work we propose a novel framework for Deep Learning that supports realtime inference of models over deep networks and networks […]

Optimal error bounds for belief functions
Optimal error bounds for belief functions – We show that our methods have the potential to lead to a more efficient inference algorithm. Our results are based on empirical measurements and our results also generalize to other domains. We do not use this algorithm in a commercial application yet, it is more suitable to commercial […]

On the Universal Approximation Problem in the Generalized Hybrid Dimension
On the Universal Approximation Problem in the Generalized Hybrid Dimension – We explore the problem of learning linear classifiers for sparse input data, which is the task of learning a latent vector from an input vector of its labels. We show empirically that we can easily learn this representation from a small set of labeled […]

Learning Representations from Machine Embedded CRF
Learning Representations from Machine Embedded CRF – Automated inference has become a vital part of any machine learning system, and it is a fundamental task for systems that perform automated inference. In this paper, we aim to design a novel method to estimate an arbitrary set of Markov models, called a setwise Bayesian inference (SBM). […]

Diversity of the Koopman Operators in the Representation of Regular Expressions
Diversity of the Koopman Operators in the Representation of Regular Expressions – We present a new approach to nonnegative matrix factorization (NMF) for supervised learning and inference in terms of nonnegative vector spaces. Our approach generalizes the existing ML methods on nonnegative spaces to fully handle negative matrix factorization. We show that, under mild conditions […]

Learning from Noisy Label Annotations
Learning from Noisy Label Annotations – We consider a supervised learning problem that aims at predicting a label’s probability of being likely to be found at a given point in time, and thus learning a sequence of labels from a set of data. While many stateoftheart performance metrics on prediction time series have been shown […]

A Comprehensive Analysis of Eye Points and Stereo Points Using a Multitemporal Hybrid Feature Model
A Comprehensive Analysis of Eye Points and Stereo Points Using a Multitemporal Hybrid Feature Model – We propose a new approach to reconstruct a face image by performing a multitemporal combination of two different spectral approaches: 3D LSTM and depth. Our method integrates the 3D LSTM and depth through a projection matrix and an image […]