Computational intelligence and neuroscience
Apr 19, 2016
Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data depende...
In this review, we discuss the emerging field of computational behavioral analysis-the use of modern methods from computer science and engineering to quantitatively measure animal behavior. We discuss aspects of experiment design important to both ob...
Neural networks : the official journal of the International Neural Network Society
Apr 12, 2016
This paper proposes a new multi-input multi-output (MIMO) transmit scheme aided by artificial neural network (ANN). The morphological perceptron with competitive learning (MP/CL) concept is deployed as a decision rule in the MIMO detection stage. The...
Neural networks : the official journal of the International Neural Network Society
Apr 12, 2016
The restricted Boltzmann machine (RBM) is an essential constituent of deep learning, but it is hard to train by using maximum likelihood (ML) learning, which minimizes the Kullback-Leibler (KL) divergence. Instead, contrastive divergence (CD) learnin...
Neural networks : the official journal of the International Neural Network Society
Mar 16, 2016
It is desirable for robots to be able to linguistically understand human actions during human-robot interactions. Previous research has developed frameworks for encoding human full body motion into model parameters and for classifying motion into spe...
IEEE transactions on pattern analysis and machine intelligence
Mar 2, 2016
This paper proposes a hierarchical clustering multi-task learning (HC-MTL) method for joint human action grouping and recognition. Specifically, we formulate the objective function into the group-wise least square loss regularized by low rank and spa...
IEEE transactions on pattern analysis and machine intelligence
Mar 2, 2016
This paper describes a novel method called Deep Dynamic Neural Networks (DDNN) for multimodal gesture recognition. A semi-supervised hierarchical dynamic framework based on a Hidden Markov Model (HMM) is proposed for simultaneous gesture segmentation...
The British journal of mathematical and statistical psychology
Mar 2, 2016
In order to look more closely at the many particular skills examinees utilize to answer items, cognitive diagnosis models have received much attention, and perhaps are preferable to item response models that ordinarily involve just one or a few broad...
In this paper, we investigate the effect of neighbourhood density (ND) on vocabulary size in a computational model of vocabulary development. A word has a high ND if there are many words phonologically similar to it. High ND words are more easily lea...