activity
20152021
most citedA Deep Reinforcement Learning Driving Policy for Autonomous Road Vehicles

5 citations · 5 across the 3 of their papers we have counts for

collaborators

7 papers

cs.LG2021

Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification

Konstantinos Makantasis, Alexandros Georgogiannis, Athanasios Voulodimos +3

An increasing number of emerging applications in data science and engineering are based on multidimensional and structurally rich data. The irregularities, however, of high-dimensi…

cs.LG2020

Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification

Konstantinos Makantasis, Athanasios Voulodimos, Anastasios Doulamis +2

Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this study, we pr…

cs.RO2019

Deep Reinforcement-Learning-based Driving Policy for Autonomous Road Vehicles

Konstantinos Makantasis, Maria Kontorinaki, Ioannis Nikolos

In this work the problem of path planning for an autonomous vehicle that moves on a freeway is considered. The most common approaches that are used to address this problem are base…

cs.RO20195 cited

A Deep Reinforcement Learning Driving Policy for Autonomous Road Vehicles

Konstantinos Makantasis, Maria Kontorinaki, Ioannis Nikolos

This work regards our preliminary investigation on the problem of path planning for autonomous vehicles that move on a freeway. We approach this problem by proposing a driving poli…

cs.LG2019

Common Mode Patterns for Supervised Tensor Subspace Learning

Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis +1

In this work we propose a method for reducing the dimensionality of tensor objects in a binary classification framework. The proposed Common Mode Patterns method takes into conside…

cs.LG2018

Tensor-based Nonlinear Classifier for High-Order Data Analysis

Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis +2

In this paper we propose a tensor-based nonlinear model for high-order data classification. The advantages of the proposed scheme are that (i) it significantly reduces the number o…