Results 61 to 70 of about 3,760 (232)

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

Domain Japanese Defective-Word Identification via BERT-GCN Semantic Expansion

IEEE Access
This study focuses on domain-specific Japanese defective-word identification and proposes a BERT–Graph Convolutional Network with Variational Autoencoder enhancement (BGCN-va) model.
Yan Liang, Muddassira Arshad
doaj   +1 more source

Constructing Dynamic Topic Models Based on Variational Autoencoder and Factor Graph

IEEE Access, 2018
Topic models are widely used in various fields of machine learning and statistics. Among them, the dynamic topic model (DTM) is the most popular time-series topic model for the dynamic representations of text corpora.
Zhinan Gou, Lixin Han, Ling Sun, Jun Zhu, Hong Yan   +4 more
doaj   +1 more source

Unsupervised generative and graph representation learning for modelling cell differentiation

Scientific Reports, 2020
Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries.
Ioana Bica, Helena Andrés-Terré, Ana Cvejic, Pietro Liò   +3 more
doaj   +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

Variational autoencoder-based spatio-temporal disentanglement for link prediction in dynamic graph

Complex & Intelligent Systems
Link prediction in dynamic graphs models real-world dynamic networks, providing a concrete and insightful representation of various scenarios. Despite recent advancements in dynamic graph learning, the factorized representations of features across ...
Peng You, Shurui Xu, Jingran Wu, Xiaohu Zhao, Jiapin Ren   +4 more
doaj   +1 more source

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +1 more source

Generalized Graph Transformer Variational Autoencoder

CoRR
Graph link prediction has long been a central problem in graph representation learning in both network analysis and generative modeling. Recent progress in deep learning has introduced increasingly sophisticated architectures for capturing relational dependencies within graph-structured data.
openaire   +2 more sources

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Automated generative process synthesis via transformer‐based dual‐loop simulation and optimization

AIChE Journal, EarlyView.
Abstract This study presents a novel framework for automated generative process synthesis, addressing the complexity of simultaneously optimizing discrete topologies and continuous operating variables. To overcome conventional superstructure limitations, we propose a dual‐loop architecture integrating generative transformers with rigorous process ...
Yeong Woo Son, Chan Kim, Ji Hun Pak, Hain Lee, Jong Min Lee   +4 more
wiley   +1 more source