Results 31 to 40 of about 83,749 (307)
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 +3 more
doaj +1 more source
Identifikasi Penulis Berdasarkan Pola Tulisan Tangan Menggunakan Convolutional Autoencoder dan KNN
Journal of Electrical Engineering and Computer, 2021 Identifikasi tulisan tangan dilakukan dengan beberapa tahapan, yaitu Akuisisi Citra dengan memanfaatkan mesin scanner dengan kualitas gambar 300dpi, Segmentasi dilakukan dengan metode threshold dan seleksi kontour dari gambar, penggabungan gambar hasil ...
Muhammad Turmudzi, Endang Setyati
doaj +1 more source
Biological Cybernetics, 2022 AbstractIn Bourlard and Kamp (Biol Cybern 59(4):291–294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called “auto-associative multilayer perceptrons”) were, in the best case, implementing singular value decomposition (SVD) Golub and Reinsch (Linear algebra, Singular value decomposition and least ...
Hervé Bourlard, Selen Hande Kabil
openaire +4 more sources
Machine Learning and Knowledge Extraction, 2023 The proliferation of novel attacks and growing amounts of data has caused practitioners in the field of network intrusion detection to constantly work towards keeping up with this evolving adversarial landscape.
Brian Lewandowski, Randy Paffenroth
doaj +1 more source
CoRR, 2020 High dimensional data is often assumed to be concentrated on or near a low-dimensional manifold. Autoencoders (AE) is a popular technique to learn representations of such data by pushing it through a neural network with a low dimension bottleneck while minimizing a reconstruction error.
Matan Atzmon, Amos Gropp, Yaron Lipman
openaire +2 more sources
CoRR, 2023 The Variational Autoencoder (VAE) is a seminal approach in deep generative modeling with latent variables. Interpreting its reconstruction process as a nonlinear transformation of samples from the latent posterior distribution, we apply the Unscented Transform (UT) -- a well-known distribution approximation used in the Unscented Kalman Filter (UKF ...
Faris Janjos +3 more
openaire +3 more sources
Autoencoding With a Classifier System [PDF]
IEEE Transactions on Evolutionary Computation, 2021 Autoencoders are data-specific compression algorithms learned automatically from examples. The predominant approach has been to construct single large global models that cover the domain. However, training and evaluating models of increasing size comes at the price of additional time and computational cost.
Richard John Preen +2 more
openaire +2 more sources
Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020 Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection.
Haoyi Fan, Fengbin Zhang, Zuoyong Li
semanticscholar +1 more source
Attention-based residual autoencoder for video anomaly detection
Applied intelligence (Boston), 2022 Automatic anomaly detection is a crucial task in video surveillance system intensively used for public safety and others. The present system adopts a spatial branch and a temporal branch in a unified network that exploits both spatial and temporal ...
Viet-Tuan Le, Yong-Guk Kim
semanticscholar +1 more source
BAE: Anomaly Detection Algorithm Based on Clustering and Autoencoder
Mathematics, 2023 In this paper, we propose an outlier-detection algorithm for detecting network traffic anomalies based on a clustering algorithm and an autoencoder model.
Dongqi Wang, Mingshuo Nie, Dongming Chen
doaj +1 more source