Anastasios Tefas | DeepAI

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Featured Co-authors

Alexandros Iosifidis
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∙ 07/14/2023

Multiplicative update rules for accelerating deep learning training and increasing robustness

Even nowadays, where Deep Learning (DL) has achieved state-of-the-art pe...

0 Manos Kirtas, et al. ∙

∙ 06/15/2023

Deep Learning for Energy Time-Series Analysis and Forecasting

Energy time-series analysis describes the process of analyzing past ener...

0 Maria Tzelepi, et al. ∙

∙ 02/12/2023

Variational Voxel Pseudo Image Tracking

Uncertainty estimation is an important task for critical problems, such ...

0 Illia Oleksiienko, et al. ∙

∙ 09/27/2022

A Novel Dataset for Evaluating and Alleviating Domain Shift for Human Detection in Agricultural Fields

In this paper we evaluate the impact of domain shift on human detection ...

0 Paraskevi Nousi, et al. ∙

∙ 06/06/2022

VPIT: Real-time Embedded Single Object 3D Tracking Using Voxel Pseudo Images

In this paper, we propose a novel voxel-based 3D single object tracking ...

3 Illia Oleksiienko, et al. ∙

∙ 03/16/2022

Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling

Deep learning methods have been employed in gravitational-wave astronomy...

0 Styliani-Christina Fragkouli, et al. ∙

∙ 08/26/2021

Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation

Knowledge Distillation has been established as a highly promising approa...

0 Maria Tzelepi, et al. ∙

∙ 08/26/2021

Quadratic mutual information regularization in real-time deep CNN models

In this paper, regularized lightweight deep convolutional neural network...

0 Maria Tzelepi, et al. ∙

∙ 08/25/2021

Semantic Scene Segmentation for Robotics Applications

Semantic scene segmentation plays a critical role in a wide range of rob...

0 Maria Tzelepi, et al. ∙

∙ 07/09/2021

Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling

Recently, artificial neural networks have been gaining momentum in the f...

0 Paraskevi Nousi, et al. ∙

∙ 05/25/2020

Attention-based Neural Bag-of-Features Learning for Sequence Data

In this paper, we propose 2D-Attention (2DA), a generic attention formul...

0 Dat Thanh Tran, et al. ∙

∙ 05/02/2020

Heterogeneous Knowledge Distillation using Information Flow Modeling

Knowledge Distillation (KD) methods are capable of transferring the know...

0 Nikolaos Passalis, et al. ∙

∙ 06/11/2019

Bag of Color Features For Color Constancy

In this paper, we propose a novel color constancy approach, called Bag o...

4 Firas Laakom, et al. ∙

∙ 02/21/2019

Deep Adaptive Input Normalization for Price Forecasting using Limit Order Book Data

Deep Learning (DL) models can be used to tackle time series analysis tas...

0 Nikolaos Passalis, et al. ∙

∙ 01/24/2019

Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data

Time series forecasting is a crucial component of many important applica...

0 Nikolaos Passalis, et al. ∙

∙ 01/16/2019

Deep Supervised Hashing leveraging Quadratic Spherical Mutual Information for Content-based Image Retrieval

Several deep supervised hashing techniques have been proposed to allow f...

0 Nikolaos Passalis, et al. ∙

∙ 11/30/2018

Style Decomposition for Improved Neural Style Transfer

Universal Neural Style Transfer (NST) methods are capable of performing ...

8 Paraskevas Pegios, et al. ∙

∙ 11/13/2018

Interactive dimensionality reduction using similarity projections

Recent advances in machine learning allow us to analyze and describe the...

20 Dimitris Spathis, et al. ∙

∙ 11/05/2018

Decoding Generic Visual Representations From Human Brain Activity using Machine Learning

Among the most impressive recent applications of neural decoding is the ...

0 Angeliki Papadimitriou, et al. ∙

∙ 10/23/2018

Using Deep Learning for price prediction by exploiting stationary limit order book features

The recent surge in Deep Learning (DL) research of the past decade has s...

0 Avraam Tsantekidis, et al. ∙

∙ 09/19/2018

Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data

Forecasting the movements of stock prices is one the most challenging pr...

0 Paraskevi Nousi, et al. ∙

∙ 03/28/2018

Probabilistic Knowledge Transfer for Deep Representation Learning

Knowledge Transfer (KT) techniques tackle the problem of transferring th...

0 Nikolaos Passalis, et al. ∙

∙ 07/25/2017

Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are well established models capable...

0 Nikolaos Passalis, et al. ∙

∙ 06/18/2017

Dimensionality Reduction using Similarity-induced Embeddings

The vast majority of Dimensionality Reduction (DR) techniques rely on se...

0 Nikolaos Passalis, et al. ∙

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