Effective Graph and Rank-based Contextual Embeddings for Textual and Multimedia Data

arXiv:2608.29001 · cs.LG, cs.CV, cs.IR · Submitted 2026-08-29 · Read on arXiv

cs.LG, cs.CV, cs.IR

Submitted: 2026-08-29

Updated: 2026-09-23

Comments: Published in International Joint Conference on Neural Networks, 2025 (IJCNN 2025). Code available in: https://github.com/thcastilho/interpretable-embeddings

DOI: 10.1109/IJCNN64981.2025.11229362

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Clustering — 29× in this paper · explained in 2 episodes
• Embeddings — 25× in this paper · explained in 2 episodes
• Transformer — 12× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Interpretability — 5× in this paper · explained in 15 episodes
• Redundancy — 5× in this paper · explained in 2 episodes
• Representation Learning — 5× in this paper · explained in 2 episodes
• Active Learning — 3× in this paper · explained in 6 episodes
• Graph Construction — 3× in this paper · explained in 3 episodes
• Affinity Propagation — 2× in this paper
• Agglomerative Hierarchical Clustering — 2× in this paper
• Feature Extraction — 2× in this paper · explained in 2 episodes
• K-Nearest Neighborhoods — 2× in this paper
• Query Performance Prediction — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Transformer Models — 2× in this paper · explained in 2 episodes
• Vector Representations — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Dimension-based Interpretable Node Embedding — 1× in this paper
• Distillation — 1× in this paper · explained in 2 episodes
• Mean Average Precision — 1× in this paper
• Normalized Mutual Information — 1× in this paper
• Quantization — 1× in this paper · explained in 12 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Simple Graph Convolution — 1× in this paper
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

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