arXiv Machine Learning July 8, 2026 By Vrushank Ahire, Yogesh Kumar, M. A. Ganaie

Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning

Read the original on arXiv Machine 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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

arXiv Machine Learning

Jul 28

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.

By Yogesh Kumar, Mudasir Ganaie

rag benchmarks

arXiv AI

Jun 2

On the Theoretical Limitations of Embedding-based Link Prediction

arXiv:2506. 22271v3 Announce Type: replace Abstract: Neural networks often map low-dimensional embeddings to high-dimensional output spaces.

By Samy Badreddine, Emile van Krieken, Luciano Serafini

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arXiv AI

Jun 18

Fully Geometric Multi-Hop Reasoning on Knowledge Graphs with Transitive Relations

arXiv:2505. 12369v2 Announce Type: replace Abstract: Multi-hop logical reasoning on knowledge graphs requires faithfully mapping the logical semantics to latent space.

By Fernando Zhapa-Camacho, Robert Hoehndorf

rag diffusion benchmarks safety

arXiv Machine Learning

Aug 4

Differentiable Lifting for Topological Neural Networks

arXiv:2608. 01160v1 Announce Type: new Abstract: Topological neural networks (TNNs) enable leveraging high-order structures on graphs (e.

By Jorge Luiz Franco, Gabriel Duarte, Alexander Nikitin, Moacir Ponti, Diego Mesquita, Amauri H. Souza

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arXiv Machine Learning

Jun 3

Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings

arXiv:2606. 03365v1 Announce Type: new Abstract: Embedding models (KGEMs) constitute the main link prediction approach to complete knowledge graphs.

By Guillaume M\'erou\'e, Fabien Gandon, Pierre Monnin

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Related stories

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

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

On the Theoretical Limitations of Embedding-based Link Prediction

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Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings