Results 91 to 100 of about 30,442 (292)

Composite Denoising Autoencoders [PDF]

, 2016
In representation learning, it is often desirable to learn features at different levels of scale. For example, in image data, some edges will span only a few pixels, whereas others will span a large portion of the image. We introduce an unsupervised representation learning method called a composite denoising autoencoder CDA to address this.
Geras, Krzysztof, Sutton, Charles
openaire   +2 more sources

OF-AE: Oblique Forest AutoEncoders

, 2023
Part 5: Learning (Active-AutoEncoders-Federated)International audienceWe propose an unsupervised ensemble method consisting of oblique trees that can address the task of auto-encoding, which is an extension of the eForest encoder introduced in [14].
Alecsa, Cristian, Daniel
core   +1 more source

A Generative Neuro‐Symbolic AI for Protein Sequence Design

Advanced Science, EarlyView.
We introduce EffieDes, a neuro‐symbolic framework coupling deep learning‐based fitness landscape parameterization with exact automated reasoning. Unlike greedy sampling, EffieDes identifies sequences that globally optimize fitness while satisfying intricate design constraints.
Marianne Defresne, Delphine Dessaux, Samuel Buchet, Lucie Barthe, Liza Ammar‐Khodja, Bessam Azizi, Valentin Durante, Gianluca Cioci, Simon de Givry, Alain Roussel, Luis F. Garcia‐Alles, Thomas Schiex, Sophie Barbe   +12 more
wiley   +1 more source

Application of autoencoders artificial neural network and principal component analysis for pattern extraction and spatial regionalization of global temperature data

Machine Learning: Science and Technology
Spatial regionalization is instrumental in simplifying the spatial complexity of the climate system. To identify regions of significant climate variability, pattern extraction is often required prior to spatial regionalization with a clustering algorithm.
Chibuike Chiedozie Ibebuchi, Omon A Obarein, Itohan-Osa Abu   +2 more
doaj   +1 more source

Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning

Aerospace, 2018
This paper introduces two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners.
Arvin Ebrahimkhanlou, Salvatore Salamone
doaj   +1 more source

StackingNet: Collective Inference Across Independent AI Foundation Models

Advanced Science, EarlyView.
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding   +4 more
wiley   +1 more source

Adversarial Autoencoders in Operator Learning [PDF]

DeepONets and Koopman autoencoders are two prevalent neural operator architectures. These architectures are autoencoders. An adversarial addition to an autoencoder have improved performance of autoencoders in various areas of machine learning.
Enyeart, Dustin, Lin, Guang
core   +1 more source

Soft, Multi‐Wavelength Photoplethysmography Enables Reliable Neonatal Blood Pressure Monitoring Via Error Stratification

Advanced Science, EarlyView.
A soft hybrid multi‐wavelength PPG wearable acquires neonatal signals. Synchronized PPG and invasive ABP data are segmented into fixed windows. A 1D‐EfficientNet model predicts segment‐level SBP and DBP. Model performance is examined with retrospective subgroup analysis across acquisition conditions.
Wenqi Shi, Lanlan Mi, Jiarong Chen, Biao Liu, Min Wang, Zisheng Dai, Bin Liu, Ruize Gao, Yunfeng Lin, Weiwei Guo, Guoxing Wang, Wenyi Luo, Cheng Chen   +12 more
wiley   +1 more source

Latent Diffusion Process With Mechanistic Guidance For Designing Functionally Graded Metamaterials With Perfect Connectivity

Advanced Science, EarlyView.
A latent diffusion‐based framework is proposed for designing functionally graded metamaterials with perfect connectivity. By integrating vector‐quantized latent representations with mechanistic guidance, the framework enables accurate inverse design toward target elastic properties.
Jongbin Yu, Dosung Lee, Namjung Kim
wiley   +1 more source

TSTScope Unifies Single‐Cell Multi‐Omics to Identify Functional T Cell States Predictive of Immunotherapy Response

Advanced Science, EarlyView.
TSTScope is an interpretable AI framework that integrates single‐cell transcriptomes with TCR information through curated gene‐program constraints. By linking receptor context to functional T cell states, it reveals response‐associated tumor‐specific T cell programs in lung cancer immunotherapy cohorts and defines an MPR score associated with ...
Shiwei Cao, Jinyu Cheng, Feng‐ao Wang, Chenxin Yi, Jiajun Chen, Keyue Wang, Lulu Liu, Junwei Liu, Yixue Li   +8 more
wiley   +1 more source