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Comparing the Learning-Model Classroom Approach, Constraint-Based Approach, and Conceptual Space – The success of deep learning systems requires a careful consideration of the complex interplay between learning and computational learning. In this work, we propose an end-to-end approach to the analysis of deep neural networks. In particular, we address the problem of finding a suitable […]

DeepDance: Video Pose Prediction with Visual Feedback – The paper presents a joint learning model for the supervised and unsupervised pose estimation problem. This involves learning a sequence of video sequences that is invariant to local motion, but that is invariant to human-like motion. The two tasks are related: the first allows to extract a […]

Directional Age Estimation from Facial Patches – In this work, we present an in-depth evaluation of two facial reconstructions using different visualizations and algorithms. The results show that facial features extracted from facial images can significantly improve the accuracy of facial facial reconstructions, outperforming the conventional methods. The performance of the models is also improved […]

Deep Multi-Objective Goal Modeling – We consider the problem of predicting the future in a single target environment through the use of the MDP. We assume that the target environment is a single image, and a prediction is performed to find the minimum distance to this image from its initial target. We derive a novel […]

Recurrent Reinforcement Learning with Spatially-Varying Recurrent Neural Networks – We propose an efficient approach to predict the next action of an action-sequence with a deep learning approach. The prediction is performed directly from an online prediction of the action-sequence from a prediction model, thus offering an efficient method for learning action-sequence based action-sequence models for […]

A Note on the GURLS constraint – This article is about a constraint to determine a probability distribution over non-convex graphs. This constraint is useful in a variety of applications, including graphs that are intractable for other constraints. The problem is to find the probability distribution of the graph in each dimension and thus efficiently […]

A deep learning model for the identification of drivers with susceptibility to fraud – We present an application of a probabilistic learning method for the identification of non-supervised and supervised drivers. Our model uses the assumption of probability distributions over the non-supervised attributes. Using a linear transformation, a probabilistic model is constructed with a non-differentiable […]

A Multi-Task Approach to Unsupervised Mobile Vehicle Detection and Localization Using Visual Cues from Social Media – In this paper, we propose a new neural network based system for the purpose of unsupervised multi-task classification based on visual-spatial descriptors. The proposed system is a convolutional neural network (CNN) capable of performing well in the task […]

A Unified View of Deep Learning – Generative models are a useful framework for achieving nonlinear learning in deep visual information-theoretic fields such as visual and speech recognition. Most current methods are based on a pre-trained neural network trained with a few examples. As a consequence, training multiple models simultaneously may not be beneficial for […]

A Survey of Sparse Spectral Analysis – We present a computational analysis of the performance of a convolutional neural network (CNN) for a multi-label classification task. It is shown that the CNN can find useful features in labeling tasks where more data is available, and can be efficiently trained by utilizing the information in labels. […]