Results 31 to 40 of about 3,896 (184)

Orthogonal Matrix-Autoencoder-Based Encoding Method for Unordered Multi-Categorical Variables with Application to Neural Network Target Prediction Problems

Applied Sciences
Neural network models, such as BP, LSTM, etc., support only numerical inputs, so data preprocessing needs to be carried out on the categorical variables to convert them into numerical data.
Yiying Wang, Jinghua Li, Boxin Yang, Dening Song, Lei Zhou   +4 more
doaj   +1 more source

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

SVD-AE: Simple Autoencoders for Collaborative Filtering

CoRR
Accepted by IJCAI ...
Seoyoung Hong 0001, Jeongwhan Choi 0002, Yeon-Chang Lee, Srijan Kumar, Noseong Park   +4 more
openaire   +3 more sources

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   +2 more sources

Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection

Remote Sensing, 2023
Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis.
Nan Wang, Yuetian Shi, Haiwei Li, Geng Zhang, Siyuan Li, Xuebin Liu   +5 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

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

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

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