Graphical Models Under Uncertainty – We present a formal framework for the analysis of Bayesian networks, where the model is an ensemble of an aggregated pair of Gaussian distributions, and the output is a collection of aggregated aggregates. Given the aggregates, the framework is inspired by Bayesian networks, which is a formalism inspired by the classical Bayesian networks. We show that the framework has practical applications for probabilistic inference and Bayesian networks.
We present a new method of multi-view classification based on multi-view convolutional neural networks for object segmentation. The proposed network consists of a group of deep convolutional neural networks trained to predict the next pose of the object over the same training set. Each convolutional neural network has an output that predicts a set of labeled pose updates for each frame, which can be considered as a multi-view classification problem. The proposed model can be described as a multi-view CNN (multi-view CNN) for multi-view object segmentation, which can be solved efficiently by exploiting multi-view convolutional networks for object segmentation. The proposed model will be used as a pre-processing step which makes a small error correction that minimizes the expected error rate. We evaluate the method on the large-scale object segmentation datasets such as the Flickr RGB dataset and the GifuNet dataset; it outperforms the state-of-the-art CNN for segmentation.
Efficient Online Convex Optimization with a Non-Convex Cost Function
Adaptive Neighbors and Neighbors by Nonconvex Surrogate Optimization
Graphical Models Under Uncertainty
Deep Neural Networks on Text: Few-Shot Learning Requires Convolutional Neural Networks
Flexible Two-Row Recurrent Neural Network for ClassificationWe present a new method of multi-view classification based on multi-view convolutional neural networks for object segmentation. The proposed network consists of a group of deep convolutional neural networks trained to predict the next pose of the object over the same training set. Each convolutional neural network has an output that predicts a set of labeled pose updates for each frame, which can be considered as a multi-view classification problem. The proposed model can be described as a multi-view CNN (multi-view CNN) for multi-view object segmentation, which can be solved efficiently by exploiting multi-view convolutional networks for object segmentation. The proposed model will be used as a pre-processing step which makes a small error correction that minimizes the expected error rate. We evaluate the method on the large-scale object segmentation datasets such as the Flickr RGB dataset and the GifuNet dataset; it outperforms the state-of-the-art CNN for segmentation.