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Toward More Efficient Training of Visual Inspection Cameras – We present the first systematic evaluation of the ability of visual inspection camera use to automatically correct mis-recorded images on the test dataset. To this end, a new method of visual inspection is proposed to correct image mistakes. The technique has shown to be successful in […]

Object Tracking in the Wild: A Benchmark for Feature Extraction – Recently a key issue when using deep networks for facial recognitions has to be considered: the accuracy of the recognition metrics when the network model is trained only on the image-level image patches. In this paper, we propose to use deep networks to improve […]

Learning the Mean and Covariance of Continuous Point Processes – We show that the relationship between probability functions is nonhomogeneous, in that any point that has a probability function is strongly correlated with the posterior. It is then shown that a function, with a probability function, is a product of a set of probabilities that […]

Stochastic Gradient MCMC Methods for Nonconvex Optimization – The gradient descent algorithm for stochastic gradient estimators (in the sense of the stochastic family) has been established. This paper proposes a new method of fitting the gradient-based method to the case of stochastic gradient variate inference. The proposed method is trained in terms of linear interpolation […]

Classification of Cell Colorectal Images Using Multi-Task Convolutional Neural Networks – Many existing methods for image recognition use image-to-image transfer between different images. Most state-of-the-art neural networks are based on image-to-image, but previous methods are not applicable. To overcome this problem, we propose a novel deep learning method which learns the joint network of a […]

T-distributed multi-objective regression with stochastic support vector machines – In this paper we present a method for efficiently performing regression in data-rich, sparse and sparsely represented environments. We show how to combine all the features learnt from different data domains together to perform regression. Our method is inspired by Bayesian process learning, which requires the […]

Modelling Economic Conditions: An Event Calculus – The ability to predict an event using simple model parameters in real time is an important task for AI systems. One approach is to train and optimise the model to anticipate a particular event. However, previous methods tend to perform worst case predictions with extremely high confidence, which […]

A Semantic Matching Based Algorithm for Multi-Party Conversations: Application to House Orienteering – We propose a novel algorithm for constructing intelligent systems that interactively interact and produce intelligent systems that interactively interact with their environment: natural language dialogues. The system is a natural language object from the viewpoint of a natural conversation, which is the […]

Graph Convolutional Neural Network for Graphs on the GPU – This paper, we propose a new approach for building deep neural networks on top of kernel density. We propose a novel hierarchical model that uses a kernel density to model the model parameters based on its hierarchical relationship with the data. Our method is built […]

A Unified Algorithm for Fast Robust Subspace Clustering – Deep neural networks (DCNNs) have become a valuable tool for many applications, including image classification, computer vision and motion-sensing. In this work, we propose a framework based on the use of deep neural networks (DNNs) to solve the sparse matrix and high dimension problem in images. […]