Results 51 to 60 of about 1,673 (179)

Bi-View Contrastive Learning with Hypergraph for Enhanced Session-Based Recommendation

Information
Session-based recommendation (SBR) aims to predict a user’s next interests based on their actions in a single visit. Recent methods utilize graph neural networks to study the pairwise relationship of item transfers, yet these often overlook the complex ...
Zijun Wang, Lai Wei
doaj   +1 more source

GBT‐SAM: A Parameter‐Efficient Depth‐Aware Model for Generalizable Brain Tumor Segmentation on mp‐MRI

International Journal of Imaging Systems and Technology, Volume 36, Issue 4, July 2026.
ABSTRACT Gliomas are aggressive brain tumors that require accurate imaging‐based diagnosis, where automated segmentation plays a central role in assessing tumor morphology and guiding treatment decisions. Manual delineation of gliomas is time‐consuming and prone to variability, motivating the use of deep learning to improve consistency and alleviate ...
Cecilia Diana‐Albelda, Roberto Alcover‐Couso, Álvaro García‐Martín, Jesus Bescos, Marcos Escudero‐Viñolo   +4 more
wiley   +1 more source

Hypergraph Computation

Engineering
Practical real-world scenarios such as the Internet, social networks, and biological networks present the challenges of data scarcity and complex correlations, which limit the applications of artificial intelligence. The graph structure is a typical tool
Yue Gao, Shuyi Ji, Xiangmin Han, Qionghai Dai   +3 more
doaj   +1 more source

CF‐SBERTHet: Collaborative and Textual Knowledge Enhanced Semantic Graphs for Sparse Recommendations

Expert Systems, Volume 43, Issue 7, July 2026.
ABSTRACT Modern e‐commerce platforms face a critical challenge: delivering accurate recommendations under extreme user–item interaction sparsity, where textual context remains systematically underutilised. Existing collaborative filtering methods degrade sharply in sparse settings, while semantic approaches fail to capture collaborative patterns ...
He Ma, Maryam Khanian Najafabadi, Qiyang Wu, WeiPing Kong, Huajun Jie, Fanye Sun, Xiaoyue Bai, Mohammadhossein Ahmadi   +7 more
wiley   +1 more source

Learning with Hypergraphs: Clustering, Classification, and Embedding [PDF]

, 2007
We usually endow the investigated objects with pairwise relationships, which can be illustrated as graphs. In many real-world problems, however, relationships among the objects of our interest are more complex than pairwise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected ...
Zhou, D., Huang, J., Schölkopf, B.
openaire   +3 more sources

Identifiability of points and rigidity of hypergraphs under algebraic constraints

Journal of the London Mathematical Society, Volume 114, Issue 1, July 2026.
Abstract The identifiability problem arises naturally in a number of contexts in mathematics and computer science. Specific instances include local or global rigidity of graphs and unique completability of partially‐filled tensors subject to rank conditions.
James Cruickshank, Fatemeh Mohammadi, Anthony Nixon, Shin‐ichi Tanigawa   +3 more
wiley   +1 more source

$$\text {H}^2\text {CAN}$$ H 2 CAN : heterogeneous hypergraph attention network with counterfactual learning for multimodal sentiment analysis

Complex & Intelligent Systems
Multimodal sentiment analysis (MSA) has garnered significant attention for its immense potential in human-computer interaction. While cross-modality attention mechanisms are widely used in MSA to capture inter-modality interactions, existing methods are ...
Changqin Huang, Zhenheng Lin, Qionghao Huang, Xiaodi Huang, Fan Jiang, Jili Chen   +5 more
doaj   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, Volume 13, Issue 35, 24 June 2026.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Learning Hypergraph-regularized Attribute Predictors [PDF]

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
This is an attribute learning paper accepted by CVPR ...
Sheng Huang 0001, Mohamed Elhoseiny, Ahmed M. Elgammal, Dan Yang 0001   +3 more
openaire   +2 more sources

Improved Multiscale Structural Mapping with Supervertex Vision Transformer for the Detection of Alzheimer's Disease Neurodegeneration

Human Brain Mapping, Volume 47, Issue 8, June 1, 2026.
We propose MSSM+, an extension of multiscale structural mapping (MSSM), together with surface supervertex mapping (SSVM) and a Supervertex Vision Transformer (SV‐ViT). Together, these methods exhibited better performance in detecting Alzheimer's disease and less variability across MR vendors than MSSM.
Geonwoo Baek, David H. Salat, Ikbeom Jang, for the Alzheimer's Disease Neuroimaging Initiative   +3 more
wiley   +1 more source