Results 111 to 120 of about 13,735 (297)

DDoS and DoS Mitigation Using a Variational Autoencoder [PDF]

, 2019
DDoS and DoS attacks have been growing in size and number over the last decade. Existing solutions employed to mitigate these attacks have proven to be inefficient in combating the problem.
Bårli, Eirik Molde
core  

De Novo Design of Membrane‐Targeting Antimicrobial Peptides Against Gram‐Negative Bacteria Using a Generative Artificial Intelligence Framework

Advanced Science, EarlyView.
Antimicrobial resistance caused by Gram‐negative bacteria remains difficult to overcome due to the protective outer membrane. To address this challenge, a multi‐condition constrained generative AI framework, GenMTAMP is proposed for de novo membrane‐targeting antimicrobial peptide design by integrating physicochemical and spatial structure descriptors.
Jingxiao Yu, Da‐Wen Sun, QingYi Wei, Yibing Zhang, Jidong Tang, Hongbin Pu   +5 more
wiley   +1 more source

Video Colorization Based on Variational Autoencoder [PDF]

This paper introduces a variational autoencoder network designed for video colorization using reference images, addressing the challenge of colorizing black-and-white videos.
Yan Liu, Xingquan Cai, Guangzi Zhang, Yulin Qian, Xiaolin Hong   +4 more
core   +1 more source

Self-Adaptive Evolutionary Info Variational Autoencoder

Computers
With the advent of increasingly powerful machine learning algorithms and the ability to rapidly obtain accurate aerodynamic performance data, there has been a steady rise in the use of algorithms for automated aerodynamic design optimisation.
Toby A. Emm, Yu Zhang
doaj   +1 more source

Sparsity in Variational Autoencoders

CoRR, 2018
An Extended Abstract of this survey will be presented at the 1st International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2019), 20-22 March 2019, Barcelona ...
openaire   +3 more sources

Accurately Deciphering Tissue Heterogeneity From Spatial Multi‐Modal and Multi‐Omics With STransformer

Advanced Science, EarlyView.
STransformer is a unified deep learning framework designed to seamlessly accommodate a comprehensive landscape of spatial data. By simultaneously capturing short‐range cellular interactions and tissue‐wide semantic patterns, it extracts robust representations to accurately dissect complex tissue heterogeneity.
Xingyi Li, Jialuo Xu, Gaoyuan Du, Xiangting Jia, Dongmin Zhao, Chunyan Zhou, Kexin Xiao, Jia Gu, Junnan Zhu, Xuequn Shang   +9 more
wiley   +1 more source

SPADE: A Deep Learning Framework for Spatial Mapping and Quantitative Cell–Cell Interaction Inference

Advanced Science, EarlyView.
SPADE integrates spatial transcriptomics with single‐cell RNA sequencing by using cell–cell communications (CCC) as a guide for spatial mapping. It improves cell‐type localization, enhances sparse gene‐expression signals, and reveals CCC programs at single‐spot resolution.
Xinyi Li, Ning Zhang, Zijie Jin
wiley   +1 more source

Single-cell RNA-Seq Clustering Based on Dual Autoencoder with Variational Bayes

Journal of Harbin University of Science and Technology
In recent years, the rapid development of single-cell RNA sequencing(scRNA-seq) technology has made it possible to research the heterogeneity of tissues and organs at the single-cell level. To accurately identify cell types in scRNA-seq data, based on
JIA Jihua, XU Yaokui, WANG Minghui
doaj   +1 more source

Poisson Variational Autoencoder

Advances in Neural Information Processing Systems 37
Published as a NeurIPS 2024 Spotlight paper (https://openreview.net/forum?id=ektPEcqGLb)
Hadi Vafaii, Dekel Galor, Jacob L. Yates
openaire   +4 more sources

The Variational Fair Autoencoder

, 2015
We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining information as possible. Our model is based on a variational autoencoding architecture with priors that encourage independence between sensitive and latent factors of ...
Louizos, C., Swersky, K., Li, Y., Welling, M., Zemel, R.   +4 more
openaire   +3 more sources