Identifying the most relevant regions in large-scale radiocarbon age assessment – 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.
In this paper, we present a new method for the estimation of the joint probability distribution of a pair of objects from image patches and the two sets of image patches. Using convolutional neural networks, the method is shown to perform well on benchmark datasets.
Pigmentation-free Registration of Multispectral Images: A Review
Learning Compact Feature Spaces with Convolutional Autoregressive Priors
Identifying the most relevant regions in large-scale radiocarbon age assessment
Boosting for Deep Supervised Learning
Theoretical Analysis of Modified Kriging for Joint PredictionIn this paper, we present a new method for the estimation of the joint probability distribution of a pair of objects from image patches and the two sets of image patches. Using convolutional neural networks, the method is shown to perform well on benchmark datasets.