Copertina

GINTRIP: Interpretable Temporal Graph Regression using Information bottleneck and Prototype-based method

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Royat, Ali, Moghadas, Seyed Mohamad, De Cruz, Lesley, Munteanu, Adrian
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

FreqFlow: Long-term forecasting using lightweight flow matching
di: Moghadas, Seyed Mohamad, et al.
Pubblicazione: (2025)

Strada-LLM: Graph LLM for traffic prediction
di: Moghadas, Seyed Mohamad, et al.
Pubblicazione: (2024)

Improving Spatio-Temporal Residual Error Propagation by Mitigating Over-Squashing
di: Moghadas, Seyed Mohamad, et al.
Pubblicazione: (2026)

Interpretable Prototype-based Graph Information Bottleneck
di: Seo, Sangwoo, et al.
Pubblicazione: (2023)

Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning Method
di: Sani, Matina Mahdizadeh, et al.
Pubblicazione: (2024)

DIRESA, a distance-preserving nonlinear dimension reduction technique based on regularized autoencoders
di: De Paepe, Geert, et al.
Pubblicazione: (2024)

Quantum Error Mitigation with Attention Graph Transformers for Burgers Equation Solvers on NISQ Hardware
di: Tousi, Seyed Mohamad Ali, et al.
Pubblicazione: (2025)

Computational bottlenecks for denoising diffusions
di: Montanari, Andrea, et al.
Pubblicazione: (2025)

Gransformer: Transformer-based Graph Generation
di: Khajenezhad, Ahmad, et al.
Pubblicazione: (2022)

TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale
di: Liu, Kay, et al.
Pubblicazione: (2024)

Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes
di: Zhao, Qiuran, et al.
Pubblicazione: (2026)

Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method
di: Wang, Siwei, et al.
Pubblicazione: (2023)

Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck
di: Seo, Sangwoo, et al.
Pubblicazione: (2024)

Interpreting Temporal Graph Neural Networks with Koopman Theory
di: Guerra, Michele, et al.
Pubblicazione: (2024)

CIP-Net: Continual Interpretable Prototype-based Network
di: Di Valerio, Federico, et al.
Pubblicazione: (2025)

Conformalized High-Density Quantile Regression via Dynamic Prototypes-based Probability Density Estimation
di: Cengiz, Batuhan, et al.
Pubblicazione: (2024)

PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis
di: Zhang, Kunyu, et al.
Pubblicazione: (2026)

Bridging Graph Structure and Knowledge-Guided Editing for Interpretable Temporal Knowledge Graph Reasoning
di: Fan, Shiqi, et al.
Pubblicazione: (2026)

Principal Prototype Analysis on Manifold for Interpretable Reinforcement Learning
di: Vamshi, Bodla Krishna, et al.
Pubblicazione: (2026)

Partial information decomposition: redundancy as information bottleneck
di: Kolchinsky, Artemy
Pubblicazione: (2024)

Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks
di: Carballo-Castro, Alba, et al.
Pubblicazione: (2024)

Multi-Stage Prototype Learning for Interpretable Time Series Classification
di: Kalisetti, Bhavesh, et al.
Pubblicazione: (2021)

Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI
di: Rana, Ashish, et al.
Pubblicazione: (2026)

Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
di: Rao, Jiahua, et al.
Pubblicazione: (2024)

Efficient Learning-based Graph Simulation for Temporal Graphs
di: Xiang, Sheng, et al.
Pubblicazione: (2025)

Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability
di: Zhang, Jiasheng, et al.
Pubblicazione: (2024)

ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts
di: Małkus, Bartłomiej, et al.
Pubblicazione: (2025)

Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning
di: Mensah, Samuel Ofosu, et al.
Pubblicazione: (2025)

Prototype Learning to Create Refined Interpretable Digital Phenotypes from ECGs
di: Sethi, Sahil, et al.
Pubblicazione: (2025)

Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification
di: Malialis, Kleanthis, et al.
Pubblicazione: (2024)

ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers
di: Romanelis, Ioannis, et al.
Pubblicazione: (2023)

SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation
di: Bazaga, Adrián, et al.
Pubblicazione: (2023)

Are Linear Regression Models White Box and Interpretable?
di: Salih, Ahmed M, et al.
Pubblicazione: (2024)

Interpretable Quantile Regression by Optimal Decision Trees
di: Lemaire, Valentin, et al.
Pubblicazione: (2026)

On the inductive bias of infinite-depth ResNets and the bottleneck rank
di: Boix-Adsera, Enric
Pubblicazione: (2025)

TimeGraphs: Graph-based Temporal Reasoning
di: Maheshwari, Paridhi, et al.
Pubblicazione: (2024)

ProtoSSL: Interpretable Prototype Learning from Unlabeled Time-Series Data
di: Song, Steven, et al.
Pubblicazione: (2026)

Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype
di: Tankovic, Nikola, et al.
Pubblicazione: (2025)

NGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation
di: Zhuang, Yuanxin, et al.
Pubblicazione: (2025)

Node Regression on Latent Position Random Graphs via Local Averaging
di: Gjorgjevski, Martin, et al.
Pubblicazione: (2024)