Results 41 to 50 of about 247,960 (211)

Missing-Insensitive Short-Term Load Forecasting Leveraging Autoencoder and LSTM

IEEE Access, 2020
In most deep learning-based load forecasting, an intact dataset is required. Since many real-world datasets contain missing values for various reasons, missing imputation using deep learning is actively studied.
Kyungnam Park, Jaeik Jeong, Dongjoo Kim, Hongseok Kim   +3 more
doaj   +1 more source

Deep Unsupervised Learning for Indoor Fire Detection Using Wi-Fi Signals

Fire
This study proposes a sensor-free approach for indoor fire detection that leverages existing Wi-Fi infrastructure as a passive sensing modality.
Sara Mostofi, Fatih Yesevi Okur, Ahmet Can Altunişik, Ertugrul Taciroğlu   +3 more
doaj   +1 more source

AE SemRL: Learning Semantic Association Rules with Autoencoders

CoRR
Association Rule Mining (ARM) is the task of learning associations among data features in the form of logical rules. Mining association rules from high-dimensional numerical data, for example, time series data from a large number of sensors in a smart environment, is a computationally intensive task.
Erkan Karabulut, Victoria Degeler, Paul Groth   +2 more
openaire   +3 more sources

ResNet Autoencoders for Unsupervised Feature Learning From High-Dimensional Data: Deep Models Resistant to Performance Degradation

IEEE Access, 2021
Efficient modeling of high-dimensional data requires extracting only relevant dimensions through feature learning. Unsupervised feature learning has gained tremendous attention due to its unbiased approach, no need for prior knowledge or expensive manual
Chathurika S. Wickramasinghe, Daniel L. Marino, Milos Manic   +2 more
doaj   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

$Ae^2I$: A Double Autoencoder for Imputation of Missing Values

, 2023
The most common strategy of imputing missing values in a table is to study either the column-column relationship or the row-row relationship of the data table, then use the relationship to impute the missing values based on the non-missing values from ...
Gao, Fuchang
core  

TC-AE: Unlocking Token Capacity for Deep Compression Autoencoders

CoRR
We propose TC-AE, a ViT-based architecture for deep compression autoencoders. Existing methods commonly increase the channel number of latent representations to maintain reconstruction quality under high compression ratios. However, this strategy often leads to latent representation collapse, which degrades generative performance. Instead of relying on
Teng Li, Ziyuan Huang, Cong Chen, Yangfu Li, Yuanhuiyi Lyu, Dandan Zheng, Chunhua Shen, Jun Zhang 0018   +7 more
openaire   +2 more sources

A Subspace Projection Approach to Autoencoder-based Anomaly Detection [PDF]

, 2023
Autoencoder (AE) is a neural network (NN) architecture that is trained to reconstruct an input at its output. By measuring the reconstruction errors of new input samples, AE can detect anomalous samples deviated from the trained data distribution.
Adarsh, Park, Jihong, Japesh, Abhinav, Choi, Jinho   +3 more
core   +1 more source

Predicting Single‐Cell Perturbation Responses Across Biological Contexts With a Deep Generative Model Integrating Optimal Transport

Advanced Science, EarlyView.
Single‐cell perturbation responses are predicted across held‐out biological contexts using scPILOT, a query‐conditioned two‐stage latent response‐transfer framework. A shared latent representation supports cell‐level response estimation by latent optimal transport, followed by Leiden‐localized query‐specific transfer and adaptive weighting.
Jialiang Wang, Ziqi Liu, Zhengqian Zhang, Yikun Cao, Junjun Ren, Peng Cheng, Jingjing Tian, Lingyun Xie, Xin Lu, Zhanwei Du, Yongzhuang Liu   +10 more
wiley   +1 more source

Bitcoin price prediction using Autoencoder-based GRU and LSTM models [PDF]

The high volatility of Bitcoin prices presents a major challenge in forecasting, as predictive models must capture both short-term fluctuations and long-term trends.
Yamasari, Yuni, Yohannes, Ervin, Nafisah, Nurun   +2 more
core   +1 more source