Intelligent Autonomous Cascades: An Extensible Approach based on Existential Rules and Beliefs – We propose a new model (the neural-network model) that employs a probabilistic model of the world and a probabilistic model of the rewards function. The model incorporates both naturalistic and quantum information into its learning procedures and achieves an accuracy in excess of 40% for the task of predicting the expected rewards of a neural network. The model, described as a semi-supervised reinforcement learning system, has been implemented on a commercial product. In our experiments, we compare the performance of all algorithms compared to the state-of-the-art on a synthetic dataset. The model learned to predict the reward of a neural network by using only the reward function alone, outperforming the best existing reinforcement learning systems on the task of predicting rewards by a similar model.
This paper presents a method for clustering in continuous time. A clustering network consists of a number of nodes which are randomly clustered. To perform clustering, a clustering algorithm is applied to the clustering network to detect nodes. The method uses a hierarchical clustering method to learn a representation of clustering network from a set of data. The clustering algorithm is trained by means of a set of data from multiple node clustering. The proposed approach has been evaluated on data from the United Kingdom and the USA. The results show that the proposed methods yield better results than state-of-the-art methods.
Toward Large-scale Computational Models
Dendritic-based Optimization Methods for Convex Relaxation Problems
Intelligent Autonomous Cascades: An Extensible Approach based on Existential Rules and Beliefs
Deep Learning for Multi-label Text Classification
Multi-View Clustering Using Hierarchical FeaturesThis paper presents a method for clustering in continuous time. A clustering network consists of a number of nodes which are randomly clustered. To perform clustering, a clustering algorithm is applied to the clustering network to detect nodes. The method uses a hierarchical clustering method to learn a representation of clustering network from a set of data. The clustering algorithm is trained by means of a set of data from multiple node clustering. The proposed approach has been evaluated on data from the United Kingdom and the USA. The results show that the proposed methods yield better results than state-of-the-art methods.