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DruGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico
Kadurin A., Nikolenko S., Khrabrov K., Aliper A., Zhavoronkov A.
with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE
Алгоритм улучшения качества КТ-изображений позвоночника человека с применением вариационного автокодировщика
Курочка, К. С., Житко, А. С.
machine learning model based on a variational autoencoder (VAE). The VAE model is designed to reduce
DruGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico
Kadurin A., Nikolenko S., Khrabrov K., Aliper A., Zhavoronkov A.
with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE
Генеративное моделирование в системе цифрового проектирования архитектуры
Гатина А.С.
variational autoencoders
Advancements in Gas Turbine Fault Detection: A Machine Learning Approach Based on the Temporal Convolutional Network–Autoencoder Model
Al-Tekreeti Khalid Fahmi Watban, Reza Kashyzadeh Kazem, Ghorbani Siamak
machine learning (ML) tools. For this purpose, an advanced Temporal Convolutional Network (TCN)–Autoencoder
Time-frequency analysis and autoencoder approach for network traffic anomaly detection
Purohit R., Kumar S., Sayyad S., Kotecha K.
transform (STFT), and autoencoders to identify anomalous network behaviour. It conducts time- frequency
MID-LEVEL FEATURES FOR AUDIO CHORD RECOGNITION USING A DEEP NEURAL NETWORK // Ученые записки КФУ. Физико-математические науки 2013 том155 N4
Glazyrin Nikolai
autoencoder
Development of Molecular Autoencoders as Generators of Protein Inhibitors: Application for Prediction of Potential Drugs Against Coronavirus SARS-CoV-2
Shuldau, M., Yushkevich, A., Bosko, I., Tuzikov, A., Andrianov, A.
A generative autoencoder for the rational design of potential inhibitors of the SARS-CoV-2 main
AutoEncoders for Denoising and Classification Applications
Assanovich, B. A., Ассанович, Б. А.
Several structures of autoencoders used for the efficient data coding with unsupervised learning
Anomaly detection using autoencoder for data quality monitoring in cloud
Dzik, C. S., Pilecki, I. I.
the original project Autoencoder that focuses on the technology of analysis, detection and forecasting poor

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