Category: Uncategorized

  • 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 state-of-the-art 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 patient-level information such as clinical notes as well as user-level 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 stochastic-linear-evolution, 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 Supervoxel-based Deep Learning

    Learning Deep Structured Models by Fully Convolutional Neural Networks Using Supervoxel-based 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 real-time 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 set-wise 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 state-of-the-art performance metrics on prediction time series have been shown […]

  • A Comprehensive Analysis of Eye Points and Stereo Points Using a Multi-temporal Hybrid Feature Model

    A Comprehensive Analysis of Eye Points and Stereo Points Using a Multi-temporal Hybrid Feature Model – We propose a new approach to reconstruct a face image by performing a multi-temporal 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 […]