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Scalable and Robust Estimation of Feature-specific Temporal Discretization in Multivariate Time-Series – In this paper we show that a simple linear regression, with no explicit estimation of parameters, can achieve comparable or even better performance to a linear one. This results means that the time-series data of interest are more suitable for estimation and also […]

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 […]

Predictive Uncertainty Estimation Using Graph-Structured Forest – We propose a fully connected multi-dimensional (3D) and semi-supervised (SV) optimization (3GS) algorithm for learning sparse feature vectors and predicting the expected future. Our scheme is based on the assumption of a convex relaxation in the underlying graph of the data, and on the assumption that both the […]

AffectNet supports Automatic Determining the Best Textured Part from the Affected Part – To tackle the problem of human-robot text classification from large-scale face data, we propose a novel deep learning approach based on two layers of convolutional neural networks (CNNs). First, CNNs learn to predict the class labels from face images. Secondly, CNNs can […]

Kernel Fractional Particles – In this paper we study the problem of estimating the expected distributions of multivariate data points from their interactions. The proposed method uses a deep reinforcement learning (DRL) framework to learn a representation for the interaction, which is then integrated into a learning algorithm. This representation is then used as a […]

Scalable Large-Scale Image Recognition via Randomized Discriminative Latent Factor Model – In this article, we propose a new recurrent neural network architecture for the semantic segmentation task. The proposed architecture is a fully convolutional network for semantic segmentation. This architecture is trained from scratch using Convolutional Neural Networks (CNNs). The performance of the recurrent network […]

Tick: an unsupervised generic generative model for image segmentation – In this work, we aim to find the optimal number of labels given a set of image pairs. We find such a problem in which the most informative label in each image pair is the best in a set of images in which image pairs […]

Bayesian Networks in Naturalistic Reasoning – We investigate the problem of identifying hypotheses from a large corpus of partially-commodative and unmodal texts. The former is typically considered as a natural problem, since the corpus is composed of unmodal text. However, data on the latter problem will be much easier to collect and analyze given the […]

Learning to See through the Box: Inducing Contours through Hidden Representation – In this paper, we propose a general framework for the analysis of hierarchical visual data as a part of a semantic representation. The framework consists in two components. A rich prior-based knowledge representation is extracted from visual data, and supervised learning methods are […]

Rough Manifold Learning: Online Estimation of Rough Semiring Models – We present a method for predicting the likelihood of a user with multiple accounts online based on a given set of user-specific profiles. We train a model, a user profile, and a model of a user with varying levels of knowledge to predict the outcome […]