Results 11 to 20 of about 64,275 (248)

Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities

Applied Sciences, 2022
Scientific applications continue to grow and produce extremely large amounts of data, which require efficient compression algorithms for long-term storage.
Jaemoon Lee, Qian Gong, Jong Choi, Tania Banerjee, Scott Klasky, Sanjay Ranka, Anand Rangarajan   +6 more
doaj   +1 more source

Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders

Mathematics, 2022
In this paper, we introduce a multi-label lazy learning approach to deal with automatic semantic indexing in large document collections in the presence of complex and structured label vocabularies with high inter-label correlation. The proposed method is
Francisco J. Ribadas-Pena, Shuyuan Cao, Víctor M. Darriba Bilbao   +2 more
doaj   +1 more source

DANAE⁺⁺: A Smart Approach for Denoising Underwater Attitude Estimation

Sensors, 2021
One of the main issues for the navigation of underwater robots consists in accurate vehicle positioning, which heavily depends on the orientation estimation phase.
Paolo Russo, Fabiana Di Ciaccio, Salvatore Troisi   +2 more
doaj   +1 more source

Autoencoding Variational Autoencoder

, 2020
Neurips ...
Cemgil, A. Taylan, Ghaisas, Sumedh, Dvijotham, Krishnamurthy, Gowal, Sven, Kohli, Pushmeet   +4 more
openaire   +2 more sources

Open Set Audio Classification Using Autoencoders Trained on Few Data

Sensors, 2020
Open-set recognition (OSR) is a challenging machine learning problem that appears when classifiers are faced with test instances from classes not seen during training.
Javier Naranjo-Alcazar, Sergi Perez-Castanos, Pedro Zuccarello, Fabio Antonacci, Maximo Cobos   +4 more
doaj   +1 more source

Detecting Anomalies of Satellite Power Subsystem via Stage-Training Denoising Autoencoders

Sensors, 2019
Satellite telemetry data contains satellite status information, and ground-monitoring personnel need to promptly detect satellite anomalies from these data.
Weihua Jin, Bo Sun, Zhidong Li, Shijie Zhang, Zhonggui Chen   +4 more
doaj   +1 more source

Feedback Recurrent Autoencoder [PDF]

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency. The recurrent structure of FRAE is designed to efficiently extract the redundancy along the time dimension and allows a compact discrete representation of the data to be ...
Yang, Yang, Sautière, Guillaume, Ryu, J. Jon, Cohen, Taco S   +3 more
openaire   +2 more sources

Hybrid Machine Learning-Based Approaches for Feature and Overfitting Reduction to Model Intrusion Patterns

Journal of Cybersecurity and Privacy, 2023
An intrusion detection system (IDS), whether as a device or software-based agent, plays a significant role in networks and systems security by continuously monitoring traffic behaviour to detect malicious activities.
Fatemeh Ahmadi Abkenari, Amin Milani Fard, Sara Khanchi   +2 more
doaj   +1 more source

Architectural Design and Performance Trade-offs of Deep Learning-Based Autoencoders for Wireless Communication Systems [PDF]

JES: Journal of Engineering Sciences
Deep learning-based autoencoders have shown significant potential in wireless communication systems by enabling the joint optimization of modulation and coding schemes.
Mohammed Abo-Zahhad, Eman Ismail, Abdelhay Ali, Osama Elnahas   +3 more
doaj   +1 more source

Deep Learning for Optical Sensor Applications: A Review

Sensors, 2023
Over the past decade, deep learning (DL) has been applied in a large number of optical sensors applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors.
Nagi H. Al-Ashwal, Khaled A. M. Al Soufy, Mohga E. Hamza, Mohamed A. Swillam   +3 more
doaj   +1 more source