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End-to-End Latent Fingerprint Search
Latent fingerprints are one of the most important and widely used sources of evidence in law enforcement and forensic agencies. The proposed end-to-end latent search system is... -
Autoencoder-Based Regression Dataset
The dataset used in this paper is a sampled dataset for autoencoder-based regression models. -
LiveJournal
Matrix factorization (MF) and Autoencoder (AE) are among the most successful approaches of unsupervised learning. -
Koopcon: A new approach towards smarter and less complex learning
The dataset condensation problem involves transforming a large-scale training set X into a smaller synthetic set X'. -
Testing Data set
The dataset used for testing the autoencoder-based false data injection attack detector. -
Training Data set
The dataset used for training the autoencoder-based false data injection attack detector. -
IEEE 118-bus system
The IEEE 118-bus system dataset is used to test the proposed reinforcement learning-based mitigation strategy for Multi-Stage Cascading Failure problem. -
XPCS Denoising Dataset (Test)
The dataset used for testing the denoising autoencoder model for noise reduction in XPCS data. -
XPCS Denoising Dataset
The dataset used for training a denoising autoencoder model for noise reduction in X-ray Photon Correlation Spectroscopy (XPCS) data. -
Asymmetrical autoencoder with a sparsifying DCST layer for gearbox sensor dat...
The proposed asymmetrical autoencoder-type neural network for gearbox sensor data compression. -
Using Decision Tree as Local Interpretable Model in Autoencoder-based LIME
The paper introduces a new version of ALIME, which uses a decision tree instead of a linear model as the locally interpretable model. -
Autoencoder& GANs for Imbalanced Multi-Omics
The proposed model is applied to two publicly available datasets, the first is the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) dataset, which contains DNA... -
Simplex Autoencoders
Synthetic data generation is increasingly important due to privacy concerns. While Autoencoder-based approaches have been widely used for this purpose, sampling from their... -
LatentGAN Autoencoder: Learning Disentangled Latent Distribution
LatentGAN Autoencoder: Learning Disentangled Latent Distribution -
Learning Not to Reconstruct Anomalies
Video anomaly detection is often seen as one-class classification (OCC) problem due to the limited availability of anomaly examples. -
OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection
Open set recognition framework for ASD-aided diagnosis -
Video Anomaly Detection by Estimating Likelihood of Representations
Video anomaly detection is a challenging task not only because it involves solving many sub-tasks such as motion representation, object localization and action recognition, but... -
Learning Graphon Autoencoders for Generative Graph Modeling
Graphon is a nonparametric model that generates graphs with arbitrary sizes and can be induced from graphs easily. Based on this model, we propose a novel algorithmic framework...
