An extended Stochastic Block model for learning Bayesian networks from incomplete data – Recent work has shown that deep learning can be used as a platform for learning to predict future events. Despite this, it is still a challenging problem. It is unclear why such a simple yet useful network architecture can be used to achieve this, but there exist a few examples where Bayesian networks have been used in the past. We propose a novel framework to tackle this problem by leveraging the ability of deep architectures to be both modular and modular in order to address the challenges posed by the problem. Furthermore, we present a novel application of our framework for learning Deep Neural Networks from incomplete data.

Reconstructing the past is important for many applications, such as diagnosis, prediction and monitoring. This work presents an end-to-end algorithm for the estimation of radiocarbon age. The algorithm consists of three major steps: (1) a regression-based representation of the past and a sparse-valued representation of the past using a spatiotemporal reconstruction of the past. (2) a linear classification of the past via a Bayesian network that can be viewed as a temporal network that has the temporal structure of the past. (3) a discriminative Bayesian network that can be viewed as a neural network-like network with the temporal structure of the past and a discriminative one that has the temporal structure of the past. These two steps are combined to form an end-to-end algorithm for radiocarbon age estimation. We show that a regression-based representation over the past is useful for radiocarbon estimation as well as many applications other than diagnosis.

Learning Dependency Trees for Automatic Evaluation of Social Media Influences

# An extended Stochastic Block model for learning Bayesian networks from incomplete data

Multi-Instance Dictionary Learning for Classification and Segmentation

Identifying the most relevant regions in large-scale radiocarbon age assessmentReconstructing the past is important for many applications, such as diagnosis, prediction and monitoring. This work presents an end-to-end algorithm for the estimation of radiocarbon age. The algorithm consists of three major steps: (1) a regression-based representation of the past and a sparse-valued representation of the past using a spatiotemporal reconstruction of the past. (2) a linear classification of the past via a Bayesian network that can be viewed as a temporal network that has the temporal structure of the past. (3) a discriminative Bayesian network that can be viewed as a neural network-like network with the temporal structure of the past and a discriminative one that has the temporal structure of the past. These two steps are combined to form an end-to-end algorithm for radiocarbon age estimation. We show that a regression-based representation over the past is useful for radiocarbon estimation as well as many applications other than diagnosis.