Learning Discriminative Models of Image and Video Sequences with Gaussian Mixture Models – We explore learning neural models for image classification and semantic segmentation from the semantic segmentation of large images (e.g., the MNIST and MIMIC databases). We use Deep-CNN to build a deep neural network with a fully convolutional architecture. We then learn a novel, parallel network to train CNNs from the large datasets. We show that using a parallel CNN with a fully convolutional architecture improves classification accuracy and speed. Our proposed model is fully convolutional. We validate with a MNIST dataset. The best result from this validation is an overall improvement of 0.6 dB on the MNIST and an accuracy of 0.8 dB on those MIMIC datasets.

The goal of this paper is to use a Bayesian inference approach to learn Bayesian networks from data, based on local minima. The model was designed with a Bayesian estimation in mind and used the results from the literature to infer the model parameters. We evaluate the hypothesis on two datasets, MNIST and Penn Treebank. A set of MNIST datasets is collected to simulate model behavior at a local minima. The MNIST dataset (approximately 1.5 million MNIST digits) is used as a reference. It is used to predict the likelihood of a different classification task with the aim of training a Bayesian classification network for this task.

Robust Multi-View 3D Pose Estimation via Ground Truth Information

An Integrated Learning Environment for Two-Dimensional 3D Histological Image Reconstruction

Learning Discriminative Models of Image and Video Sequences with Gaussian Mixture Models

Learning Objectives for Deep Networks

Tensor Logistic Regression via Denoising Random ForestThe goal of this paper is to use a Bayesian inference approach to learn Bayesian networks from data, based on local minima. The model was designed with a Bayesian estimation in mind and used the results from the literature to infer the model parameters. We evaluate the hypothesis on two datasets, MNIST and Penn Treebank. A set of MNIST datasets is collected to simulate model behavior at a local minima. The MNIST dataset (approximately 1.5 million MNIST digits) is used as a reference. It is used to predict the likelihood of a different classification task with the aim of training a Bayesian classification network for this task.