Even nowadays, where Deep Learning (DL) has achieved state-of-the-art
pe...
Uncertainty estimation is an important task for critical problems, such ...
In this paper we evaluate the impact of domain shift on human detection
...
In this paper, we propose a novel voxel-based 3D single object tracking ...
Deep learning methods have been employed in gravitational-wave astronomy...
In this paper, we consider the problem of non-linear dimensionality redu...
Recently, artificial neural networks have been gaining momentum in the f...
spectral-based subspace learning is a common data preprocessing step in ...
In this paper, we propose 2D-Attention (2DA), a generic attention formul...
Rescue vessels are the main actors in maritime safety and rescue operati...
Knowledge Distillation (KD) methods are capable of transferring the know...
In this paper we propose a deep learning method for performing
attribute...
Security monitoring via ubiquitous cameras and their more extended in
in...
In this paper, we propose a novel color constancy approach, called Bag o...
Deep Learning (DL) models can be used to tackle time series analysis tas...
Time series forecasting is a crucial component of many important
applica...
Several deep supervised hashing techniques have been proposed to allow f...
Universal Neural Style Transfer (NST) methods are capable of performing ...
Recent advances in machine learning allow us to analyze and describe the...
Among the most impressive recent applications of neural decoding is the
...
The recent surge in Deep Learning (DL) research of the past decade has
s...
Forecasting the movements of stock prices is one the most challenging
pr...
Knowledge Transfer (KT) techniques tackle the problem of transferring th...
Convolutional Neural Networks (CNNs) are well established models capable...
The vast majority of Dimensionality Reduction (DR) techniques rely on
se...