Results 101 to 110 of about 30,442 (292)

Artificial Neural Network AutoEncoders an Overview

, 2022
AutoEncoders are the simplest and most powerful type of Artificial Neural Network in Artificial Intelligence that can be applied in multiple domains.AutoEncoders are Artificial Neural Network structures that are used with the purpose of extracting ...
Diyako_Kaso
core   +1 more source

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

Advanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi, Ahmadreza Argha, Nona Farbehi, Ivan Bakhshayeshi, Thantrira Porntaveetus, Youqiong Ye, Nigel H. Lovell, Hamid Alinejad‐Rokny   +7 more
wiley   +1 more source

Aplicación de autoencoders y autoencoders variacionales al sparse index tracking del S&P100 [PDF]

This article explores the use of autoencoders (AE) and variational autoencoders (VAE) to address the sparse index tracking problem applied to the S&P100 index.
Aragón Urrego, Daniel
core   +1 more source

A Site‐Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design

Advanced Science, EarlyView.
MolDBG is a site‐aware, sequence‐only framework that unifies drug‐target affinity prediction, binding‐site identification, and affinity‐conditioned molecular generation for structured proteins. Guided by multi‐task binding‐site supervision, it aligns interaction‐critical residues before learning drug‐target representations and simultaneously infers ...
Gang Luo, Qianqian Zhang, Chenhao Wang, Zhiheng Yi, Alex Jinpeng Wang, Jing Tang, Min Li   +6 more
wiley   +1 more source

Implicit Autoencoders

CoRR, 2018
In this paper, we describe the "implicit autoencoder" (IAE), a generative autoencoder in which both the generative path and the recognition path are parametrized by implicit distributions. We use two generative adversarial networks to define the reconstruction and the regularization cost functions of the implicit autoencoder, and derive the learning ...
openaire   +2 more sources

STWave: Fine‐Scale Spatial Structure Discovery in Microscopic‐Resolution Spatial Transcriptomics via Patchwise Wavelet Graphs

Advanced Science, EarlyView.
STWave transforms massive microscopic‐resolution spatial transcriptomics into interpretable fine‐scale tissue maps through patch‐wise inference, wavelet‐based multi‐scale encoding, and dual‐domain reconstruction. It reduces noise while preserving weak spatial signals, enabling efficient analysis of 6 40 000 spots of 2.47 GB GPU memory and revealing ...
Tao Jiang, Songming Zhang, Xiaofeng Chen, Xiongtao Xiao, Wenming Cao, Weikai Li, Tong Zhao, Bing Li, Xinyue Xu, Zhongshan Li   +9 more
wiley   +1 more source

Autoencoding topology

CoRR, 2018
10 ...
openaire   +3 more sources

Uncoupling Type I Interferon Benefits From Inflammatory Toxicity: Transformer‐Prioritized Precision Agonists for Potent and Safer Cancer Immunotherapy

Advanced Science, EarlyView.
A Transformer‐based AI framework, DLINP, screens millions of compounds to identify Co68, a cobalt‐pincer organometallic complex that biases TLR4‐MD2 signaling toward antitumor interferon activation while suppressing inflammatory toxicity through an early TLR4‐SYK‐STAT1 axis.
Xuefei Guo, Yang Zhao, Xianle Rong, Xingyu Chen, Xiao Wang, Tianyi Liu, Yunfei Xie, Yushu Zou, Pingsen Zhao, Qiang Liu, Fuping You   +10 more
wiley   +1 more source

Gradient Boosted Trees and Denoising Autoencoder to Correct Numerical Wave Forecasts

Journal of Marine Science and Engineering
This paper is dedicated to correcting the WAM/ICON numerical wave model predictions by reducing the residue between the model’s predictions and the actual buoy observations. The two parameters used in this paper are significant wave height and wind speed.
Ivan Yanchin, C. Guedes Soares
doaj   +1 more source

Predicting Single‐Cell Perturbation Responses Across Biological Contexts With a Deep Generative Model Integrating Optimal Transport

Advanced Science, EarlyView.
Single‐cell perturbation responses are predicted across held‐out biological contexts using scPILOT, a query‐conditioned two‐stage latent response‐transfer framework. A shared latent representation supports cell‐level response estimation by latent optimal transport, followed by Leiden‐localized query‐specific transfer and adaptive weighting.
Jialiang Wang, Ziqi Liu, Zhengqian Zhang, Yikun Cao, Junjun Ren, Peng Cheng, Jingjing Tian, Lingyun Xie, Xin Lu, Zhanwei Du, Yongzhuang Liu   +10 more
wiley   +1 more source