Recurrent Residual Networks for Accurate Image Saliency Detection – In this work we present a novel recurrent neural network architecture, Residual network, for Image Residual Recognition (ResIST), which aims to learn latent features from unlabeled images, which is commonly used for training ResIST. The ResIST architecture was designed to be flexible to overcome the limitation of traditional ResIST architectures such as ResNet, by leveraging the deep latent representations to perform the inference task. We propose a novel architecture that learns the latent features according to its labels, based on an effective learning mechanism to improve the performance. On the other hand, it achieves the same performance without additional expensive training time. We experiment the ResIST architecture on three datasets, namely, MNIST, PASCAL VOC and ILSVRC 2017 ResIST dataset, and we obtain a novel competitive results.
The question in the literature has been: How can we learn to build a human-computer joint, and that can be exploited for intelligent artificial systems? On this front, in this work we provide two answers, namely, a probabilistic model and a graphical model of human intention. The probabilistic model can be interpreted by an intuitive user as the combination of human and computer intent and the graphical model as the combination of human and computer intent in the form of an ontology. In the graphical model, the human is modeled by a hierarchical ontology representing a hierarchy. The human is represented as a complex graphical model, which provides a graphical model that can be interpreted as the combined of human and computer intentions. The graphical model, which has not been considered in the literature, makes the task of constructing intelligent and complete systems contingent on a careful assessment of the human intention. In this work, we give a practical view on the design of intelligent and complete systems and show that it is crucial to make use of the knowledge of human intention and the human intention.
A new analysis of the semantic networks underlying lexical variation
Learning to Make Predictions under a Budget
Recurrent Residual Networks for Accurate Image Saliency Detection
On the Reliability of Convolutional Belief Networks: A Randomized Bayes Approach
A Bayesian Model for Data Completion and Relevance with Structured Variable EliminationThe question in the literature has been: How can we learn to build a human-computer joint, and that can be exploited for intelligent artificial systems? On this front, in this work we provide two answers, namely, a probabilistic model and a graphical model of human intention. The probabilistic model can be interpreted by an intuitive user as the combination of human and computer intent and the graphical model as the combination of human and computer intent in the form of an ontology. In the graphical model, the human is modeled by a hierarchical ontology representing a hierarchy. The human is represented as a complex graphical model, which provides a graphical model that can be interpreted as the combined of human and computer intentions. The graphical model, which has not been considered in the literature, makes the task of constructing intelligent and complete systems contingent on a careful assessment of the human intention. In this work, we give a practical view on the design of intelligent and complete systems and show that it is crucial to make use of the knowledge of human intention and the human intention.