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Deep Neural Networks Based on Random Convex Functions – In this paper we provide an exhaustive analysis of the problem of learning a random matrix, using a single fixed-rank matrix to provide a good discriminative measure. The problem of learning a matrix from a discrete matrix is discussed, and the learning process based on the […]

View-Tern Methods for the Construction of a High-Order Hidden Dataset – An expert in the field of machine learning has the ability to tell which model is more effective than another. A natural way of measuring the effectiveness of this approach is to use the average of the model parameters in the set of model […]

Unsupervised Unsupervised Domain Adaptation for Robust Low-Rank Metric Learning – We propose a method that is more robust than other methods by learning sparse representations of a domain. This is the first time that the data is sparsely sparse. The key finding of this paper comes from the fact that the sparse representation of the […]

Visual Tracking using Visual Tensor Factorization with Applications to Automated Vehicle Analysis and Tracking – We propose a new 3D-MAP method for semantic vehicle location based on spatial similarity map that aims to maximize the information gained by the 2D camera-based system. Based on the spatial similarity map, the system utilizes 3D point-based detection of […]

Auxiliary Model Embedding for Constrained Constrained Object Localization – An efficient and efficient inference method for object localization and tracking is sought. The method assumes that a robot performs its movement of the body in a consistent manner and the localization and tracking are synchronized simultaneously in a manner consistent with the robot body position, […]

Anomaly Detection using Knowledge Graphs – We present a new approach, Detection of the anomaly by Knowledge Graphs (PDG), to identify the existence of anomalies in a dataset by exploiting some prior knowledge which can be useful at developing novel solutions. By exploiting the knowledge that the input data has been observed in the past, […]

PupilNet: Principled Face Alignment with Recurrent Attention – In this paper, we propose an attention-based model for visual attention. Previous work explicitly uses the attention mechanism to learn attention maps instead of a feature. However, previous studies focused on the visual attention mechanism which was not explored. Here, we explore the visual attention mechanism using […]

Learning a chain in a deep neural network – This chapter deals with a new technique based on the concept of the Bayesian posterior. We analyze the Bayesian posterior for some applications: classification, regression, and classification of complex variables. We show that the posterior is consistent with uncertainty in a Bayesian network and that our […]

Probability Space for Estimation of Causal Interactions – The probabilistic and the temporal information of the causal interactions with random variables are often used as a regularizer for reasoning about the underlying structure of the data, i.e. the distribution of beliefs in the data. However, it is known that beliefs are not always reliable and […]

Recurrent Online Prediction: A Stochastic Approach – We provide an approach to learn latent vector representations at multiple scales, which in turn learns the latent representation of the data of interest over the data of interest. Our learning algorithm requires a finite size and a large number of samples, and is based on a convex […]