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Molex optimization for 3D calibration of 3D-printed clothing: a real-world application – This paper gives an overview of several aspects of 3D calibration algorithms and their applications. We are the first to provide an overview of these algorithm’s capabilities compared to state-of-the-art 3D calibration algorithms. We then provide a comparative analysis of the performance of […]

A Robust Method for Non-Stationary Stochastic Regression – Learning structured models requires an effective and efficient method to learn a model which is useful for modeling large-scale data data. The purpose of this study is to design a robust method to model data with multiple dimensions. Given a data set and a large representation space, […]

A Deep Learning Model for Multiple Tasks Teleoperation – Deep neural networks are used widely for both the task-driven and the task-driven tasks. The latter is an important area in computer science and medicine. In this paper, we show how a fully recurrent network – a subnet of a neural network – can be used […]

Comparing Deep Neural Networks to Matching Networks for Age Estimation – We present a novel model for age estimation in supervised learning where the task of age estimation is to estimate a new set of informative features (with respect to a set of relevant age labels on that set) from data collected from a population […]

Learning User Preferences to Automatically Induce User Preferences from Handcrafted Conversational Messages – In this work, we first show that a learning algorithm with a low-rank priors matrix is able to learn a preference from its raw input data using only high-rank priors. The algorithm learns a high-rank priors matrix which is used in the […]

The Kernelized k-means algorithm: Unsatisfiability and approximate convergence – We discuss an algorithm for sparse regression with noisy input data based on the assumption that the input space is sparse, and the noisy output space is sparse. Although this algorithm has been extensively used for sparse regression, the main drawback of its approach is that […]

A novel fuzzy clustering technique based on minimum parabolic filtering and prediction by distributional evolution – In this paper we investigate the impact of the random variable on the performance of neural-network units (NNs) in supervised learning. Given a sequence of NNs and a random vector as input, the training set is trained using a […]

Predicting the expected speed of approaching vehicles using machine learning – This work explores the applications of machine learning based models in computer vision. A common and important goal of machine learning is to predict the vehicle’s speed, acceleration, and odometry. The machine learning approach is very useful for automatically detecting collisions and detecting vehicle […]

A Structural Recurrent Encoder – This paper presents a supervised learning framework for learning visual modal representations from human human video. This model is developed to model the perceptual and semantic dynamics for a video. In this framework, the interaction between the human and the video is represented as a sequential event. The human interactions […]

Optimal Regret Bounds for Gaussian Processical Least Squares – This paper presents a novel approach for multi-task learning. Based on the structure to be modeled by a nonlinear dynamical system, the proposed approach relies on a nonlinear representation in a nonlinear dynamical system, which is expressed by a convex optimization problem. In the formulation, the […]