arXiv Machine Learning

XGRVFL-MV: Residual-Coupled Graph-Embedded Multi-View Random Vector Functional Link Network with FleXi Guardian Loss

arXiv:2607. 23149v1 Announce Type: new Abstract: Random Vector Functional Link (RVFL) networks provide an efficient randomized learning framework for classification.

arXiv Machine Learning

Jul 31

Adaptive Weighted LSSVM for Multi-View Classification

arXiv:2512. 02653v2 Announce Type: replace Abstract: Multi-view learning integrates diverse representations of the same instances and can improve performance when interactions across views are effectively exploited.

By Farnaz Faramarzi Lighvan, Mehrdad Asadi, Lynn Houthuys

benchmarks

arXiv Machine Learning

Jul 31

DAS-PMVC: A Framework for Partial Multi-View Clustering via Dual Alignment and Structure Enhancement

arXiv:2607. 27761v1 Announce Type: new Abstract: In recent years, multi-view clustering has attracted widespread research interest.

By Shubin Ma, Liang Zhao, Chuanye He, Zhenjiao Liu, Liang Zou, Lin Yuanbo Wu, Yu Shao

rag diffusion benchmarks safety

arXiv Machine Learning

Jul 8

Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning

arXiv:2607. 05635v1 Announce Type: new Abstract: Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities.

By Vrushank Ahire, Yogesh Kumar, M. A. Ganaie

rag benchmarks

arXiv Machine Learning

2d ago

Robust Dual-Model Collaborative Random Vector Functional Link Network

arXiv:2608. 13628v1 Announce Type: new Abstract: Random vector functional link (RVFL) networks are lightweight and fast neural models that offer efficient training and strong generalization through randomized hidden-layer weights and direct input-output connections.

By A. Quadir, A. Rahaman, Mushir Akhtar, M. Tanveer

benchmarks

arXiv Machine Learning

Jul 15

Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence

arXiv:2607. 12916v1 Announce Type: new Abstract: In this work, we introduce CoCo, a loss function aimed at learning normalized and well-structured representations.

By Blanca Cano-Camarero, \'Angela Fern\'andez-Pascual, Jos\'e R. Dorronsoro

rag benchmarks

arXiv Machine Learning

5d ago

Multiview Representation Learning via Distributed Joint Latent Space Structuring

arXiv:2504. 18455v2 Announce Type: replace-cross Abstract: We study distributed multiview representation learning, a problem in which $K$ clients each observe a distinct but possibly statistically correlated view.

By Milad Sefidgaran, Piotr Krasnowski, Abdellatif Zaidi

diffusion safety

Hugging Face Trending Papers

Jul 14

Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence

In this work, we introduce CoCo, a loss function aimed at learning normalized and well-structured representations. The proposed loss encourages intra-class collapse and inter-class contrast while preserving sufficient flexibility for neural networks to approximate geometrically optimal embeddings with large angular separation between classes.

rag benchmarks

arXiv AI

Aug 12

Sheaf-Based Federated Representation Learning

arXiv:2608. 10016v1 Announce Type: cross Abstract: Heterogeneous federated systems require agents to learn and exchange informative representations despite differences in data distributions, sensing modalities, model architectures, latent dimensionalities, and local learning objectives.

By Gabriele D'Acunto, Enrico Grimaldi, Valeria Avino, Mario Edoardo Pandolfo, Leonardo Di Nino, Sergio Barbarossa, Paolo Di Lorenzo

rag agents diffusion safety

arXiv Machine Learning

Jul 28

Unsupervised Graph Representation Learning with Complementary View Alignment

arXiv:2607. 24338v1 Announce Type: new Abstract: Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data.

By Zengyi Wo, Shiyu Zhang, Qiyao Peng, Tianpeng Li, Xuan Guo

rag benchmarks safety

arXiv Machine Learning

Jul 22

One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models

arXiv:2607. 19128v1 Announce Type: new Abstract: Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data remains largely unexplored.

By Jiayi Yang, Yifang Chen, Yuanfu Sun, Jiajin Liu, Qiaoyu Tan

llms rag multimodal benchmarks

arXiv Machine Learning

Aug 4

Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity

arXiv:2608. 00623v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopted across diverse domains.

By Yinlin Zhu, Di Wu, Yi Zhang, Xunkai Li, Wang Luo, Wei-Jin Huang, Miao Hu, Guocong Quan

fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 21

Graph-Embedded Intuitionistic Fuzzy Broad Learning System: A Multi-view Framework

arXiv:2607. 16728v1 Announce Type: new Abstract: The Broad Learning System (BLS) has been widely used for data classification and is based on a layer-by-layer feed-forward structure.

By Yogesh Kumar, Manju, Mudasir Ganaie

rag benchmarks

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