Fast deep convolutional face detection in the wild exploiting hard sample mining
Face detection constitutes a key visual information analysis task in Machine Learning. The
rise of Big Data has resulted in the accumulation of a massive volume of visual data which …
rise of Big Data has resulted in the accumulation of a massive volume of visual data which …
Deep autoencoders for attribute preserving face de-identification
The mass availability of mobile devices equipped with cameras has lead to increased public
privacy concerns in recent years. Face de-identification is a necessary first step towards …
privacy concerns in recent years. Face de-identification is a necessary first step towards …
Hybrid neurofuzzy investigation of short-term variability of wind resource in site suitability analysis: a case study in South Africa
PA Adedeji, SA Akinlabi, N Madushele… - Neural Computing and …, 2021 - Springer
Energy generation from wind resources is now a mature technology with the ability to
compete with traditional energy sources at utility scales in many countries, through the …
compete with traditional energy sources at utility scales in many countries, through the …
A deep learning model for fault diagnosis with a deep neural network and feature fusion on multi-channel sensory signals
Q Ye, S Liu, C Liu - Sensors, 2020 - mdpi.com
Collecting multi-channel sensory signals is a feasible way to enhance performance in the
diagnosis of mechanical equipment. In this article, a deep learning method combined with …
diagnosis of mechanical equipment. In this article, a deep learning method combined with …
Self-supervised autoencoders for clustering and classification
Clustering techniques aim at finding meaningful groups of data samples which exhibit
similarity with regards to a set of characteristics, typically measured in terms of pairwise …
similarity with regards to a set of characteristics, typically measured in terms of pairwise …
Manifold regularized stacked denoising autoencoders with feature selection
J Yu - Neurocomputing, 2019 - Elsevier
This paper proposes a new stacked denoising autoencoders (SDAE), called manifold
regularized SDAE (MRSDAE) based on particle swarm optimization (PSO), where manifold …
regularized SDAE (MRSDAE) based on particle swarm optimization (PSO), where manifold …
Deep multi-view learning to rank
We study the problem of learning to rank from multiple information sources. Though multi-
view learning and learning to rank have been studied extensively leading to a wide range of …
view learning and learning to rank have been studied extensively leading to a wide range of …
Autoencoder-driven spiral representation learning for gravitational wave surrogate modelling
Recently, artificial neural networks have been gaining momentum in the field of gravitational
wave astronomy, for example in surrogate modelling of computationally expensive …
wave astronomy, for example in surrogate modelling of computationally expensive …
Concept detection and face pose estimation using lightweight convolutional neural networks for steering drone video shooting
N Passalis, A Tefas - 2017 25th European Signal Processing …, 2017 - ieeexplore.ieee.org
Unmanned Aerial Vehicles, also known as drones, are becoming increasingly popular for
video shooting tasks since they are capable of capturing spectacular aerial shots. Deep …
video shooting tasks since they are capable of capturing spectacular aerial shots. Deep …
Machine Learning Applications in Gravitational Wave Astronomy
N Stergioulas - Compact Objects in the Universe, 2024 - Springer
Gravitational wave astronomy has emerged as a new branch of observational astronomy,
since the first detection of gravitational waves in 2015. The current number of O (100) …
since the first detection of gravitational waves in 2015. The current number of O (100) …