Results 11 to 20 of about 3,700 (193)
OF-AE: Oblique Forest AutoEncoders [PDF]
, 2023 In the present work we propose an unsupervised ensemble method consisting of oblique trees that can address the task of auto-encoding, namely Oblique Forest AutoEncoders (briefly OF-AE). Our method is a natural extension of the eForest encoder introduced in [1].
Cristian Daniel Alecsa
+6 more sources
AE-Flow: AutoEncoder Normalizing Flow [PDF]
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 Recently normalizing flows have been gaining traction in text-to-speech (TTS) and voice conversion (VC) due to their state-of-the-art (SOTA) performance. Normalizing flows are unsupervised generative models. In this paper, we introduce supervision to the training process of normalizing flows, without the need for parallel data.
Jakub Mosiński +4 more
openalex +3 more sources
Idea of AE separation from unpredicted source area during AE testing by autoencoder [PDF]
Proceedings of 1st International Electronic Conference on Applied Sciences, 2020 When conducting AE testing, there is an industrial need to separate AE from monitoring area to that from outside of the area in some cases. In this study, usefulness of autoencoder to solve this problem is discussed by simple experiment using an isotropic thin steel ruler.
Yoshihiro Mizutani
openalex +2 more sources
$Ae^2I$: A Double Autoencoder for Imputation of Missing Values [PDF]
, 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 other columns of the same row, or from the other rows of the same column.
Fuchang Gao
openalex +3 more sources
ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image Representations [PDF]
, 2023 Self-supervised learning has attracted increasing attention as it learns data-driven representation from data without annotations. Vision transformer-based autoencoder (ViT-AE) by He et al. (2021) is a recent self-supervised learning technique that employs a patch-masking strategy to learn a meaningful latent space. In this paper, we focus on improving
Chinmay Prabhakar +5 more
openalex +3 more sources
AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection [PDF]
IEEE Access, 2023 Structured Query Language (SQL) injection attacks represent a critical threat to database-driven applications and systems, exploiting vulnerabilities in input fields to inject malicious SQL code into database queries. This unauthorized access enables attackers to manipulate, retrieve, or even delete sensitive data.
Nisrean Thalji +4 more
openalex +3 more sources
SVD-AE: Simple Autoencoders for Collaborative Filtering [PDF]
Accepted by IJCAI ...
Seoyoung Hong +4 more
openalex +3 more sources
AE SemRL: Learning Semantic Association Rules with Autoencoders [PDF]
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 +2 more
openalex +3 more sources
SMALL-DATA REDUCED-ORDER MODELING OF CHAOTIC DYNAMICS THROUGH SYCO-AE: SYNTHETICALLY CONSTRAINED AUTOENCODERS [PDF]
Journal of Machine Learning for Modeling and ComputingData-driven reduced-order modeling of chaotic dynamics can result in systems that either dissipate or diverge catastrophically. Leveraging nonlinear dimensionality reduction of autoencoders and the freedom of nonlinear operator inference with neural networks, we aim to solve this problem by imposing a synthetic constraint in the reduced-order space ...
Andrey A. Popov, Renato Zanetti
openalex +3 more sources