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Design of Novel Inter-rater Agreement and Boundary Detection Using Nonmonotonic Constraints – Recent work on supervised learning of multiview visual systems has focused on finding visually rich subregions of a visual system. There are many approaches in this area, such as the use of deep neural networks (DNNs), deep convolutional networks (CNN), or even semi-supervised […]

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 […]

Feature-Augmented Visuomotor Learning for Accurate Identification of Manipulating Objects – This paper describes a simple, yet effective technique to detect object-specific behaviors from deep networks of object-sensitive photometric sensors. An attention mechanism is designed to guide object detection by leveraging photometric information provided by object features. The attention mechanism is implemented by using a deep […]

Robust 3D Registration via Deep Generative Models – We present a supervised 2D autoencoder for the task of 3D reconstruction. Our model consists of two separate convolutional networks with a recurrent feed-forward network to encode image data sequences, as well as a recurrent network to represent the visual information for the model. We then extract […]

An Open Source Framework for Video Processing from Natural Scene Data – In this paper, we propose a new approach for extracting visual concepts from the observed scene. We first extract the scene features, and then use a deep neural network to extract the semantic features. The proposed approach is based on minimizing the variance […]

Dependency-Based Deep Recurrent Models for Answer Recommendation – This paper studies the effect of two different types of information: (1) context and (2) message. Given a set of data, a text is associated with the context of that text, and the message is the message represented by texts. In this paper, we apply Convolutional Neural […]

An Action Probability Model for Event Detection from Data Streams – Object-Poster system (PDS) provides a mechanism for generating a set of candidate objects based on a set of information encoded from object images. In this paper, we propose an efficient PDS method based on a fully-validated classifier. The proposed method is capable of generating […]

Learning to Disambiguate with Generative Adversarial Programming – This paper examines the question of how to model and optimize a Bayesian network trained on an input data set for predicting whether a user will visit the website of a pharmaceutical company in a given time period. This is a task that is usually tackled by […]

Predicting the outcomes of games – In this paper, we develop a method of using conditional independence (CaI) and conditional independencies (CaIn) to model both the expected outcomes of games and their rewards. The CaI based model achieves the highest expected outcomes of games with CaIn and Low CaIn. The CaI based model has several […]

On the Relation between Bayesian Decision Trees and Bayesian Classifiers – In this work we present a novel method for predicting the performance of a Bayesian classifier by considering the likelihood of the class of the data, while using the class model on a probability distribution over the probability distribution of the classification labels. We […]