Imagen de Portada

Explaining Clustering of Ecological Momentary Assessment Data Through Temporal and Feature Attention

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Ntekouli, Mandani, Spanakis, Gerasimos, Waldorp, Lourens, Roefs, Anne
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting
por: Ntekouli, Mandani, et al.
Publicado: (2024)

Developing a transdiagnostic Ecological Momentary Assessment protocol for psychopathology
por: Alberto Jover Martínez, et al.
Publicado: (2024)

Analyzing the Attention Heads for Pronoun Disambiguation in Context-aware Machine Translation Models
por: Mąka, Paweł, et al.
Publicado: (2024)

You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models
por: Mąka, Paweł, et al.
Publicado: (2025)

Sequence Shortening for Context-Aware Machine Translation
por: Mąka, Paweł, et al.
Publicado: (2024)

Navigating WebAI: Training Agents to Complete Web Tasks with Large Language Models and Reinforcement Learning
por: Thil, Lucas-Andreï, et al.
Publicado: (2024)

Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
por: da Silva, Matheus Camilo, et al.
Publicado: (2026)

Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessment
por: Yao, Yue, et al.
Publicado: (2025)

Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention
por: Koistinen, Kosti, et al.
Publicado: (2026)

LegalLens Shared Task 2024: Legal Violation Identification in Unstructured Text
por: Hagag, Ben, et al.
Publicado: (2024)

Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data
por: Li, Lehao, et al.
Publicado: (2026)

Predicting and Explaining Traffic Crash Severity Through Crash Feature Selection
por: Castellani, Andrea, et al.
Publicado: (2025)

AttentiveGRUAE: An Attention-Based GRU Autoencoder for Temporal Clustering and Behavioral Characterization of Depression from Wearable Data
por: Soley, Nidhi, et al.
Publicado: (2025)

Explaining Kernel Clustering via Decision Trees
por: Fleissner, Maximilian, et al.
Publicado: (2024)

Explaining Concept Shift with Interpretable Feature Attribution
por: Lyu, Ruiqi, et al.
Publicado: (2025)

B-TGAT: A Bi-directional Temporal Graph Attention Transformer for Clustering Multivariate Spatiotemporal Data
por: Nji, Francis Ndikum, et al.
Publicado: (2025)

Dutch CrowS-Pairs: Adapting a Challenge Dataset for Measuring Social Biases in Language Models for Dutch
por: Strazda, Elza, et al.
Publicado: (2025)

Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data
por: Nji, Francis Ndikum, et al.
Publicado: (2024)

Mixture of Attentions For Speculative Decoding
por: Zimmer, Matthieu, et al.
Publicado: (2024)

Quantum Feature Optimization for Enhanced Clustering of Blockchain Transaction Data
por: Tsai, Yun-Cheng, et al.
Publicado: (2025)

Attention-Guided Deep Adversarial Temporal Subspace Clustering (A-DATSC) Model for multivariate spatiotemporal data
por: Nji, Francis Ndikum, et al.
Publicado: (2025)

Assessment of Spatio-Temporal Predictors in the Presence of Missing and Heterogeneous Data
por: Zambon, Daniele, et al.
Publicado: (2023)

Explaining Temporal Graph Predictions With Shapley Values
por: Sussek, Lea-Marie, et al.
Publicado: (2026)

Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation
por: Wei, Hui, et al.
Publicado: (2024)

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning
por: Shokry, Ahmed, et al.
Publicado: (2025)

From Weight Perturbation to Feature Attribution for Explaining Fully Connected Neural Networks
por: Lymperopoulos, Thodoris, et al.
Publicado: (2026)

Sequential Attention for Feature Selection
por: Yasuda, Taisuke, et al.
Publicado: (2022)

Predicting and Explaining Hearing Aid Usage Using Encoder-Decoder with Attention Mechanism and SHAP
por: Su, Qiqi, et al.
Publicado: (2024)

Distribution-Based Feature Attribution for Explaining the Predictions of Any Classifier
por: Li, Xinpeng, et al.
Publicado: (2025)

Explaining Categorical Feature Interactions Using Graph Covariance and LLMs
por: Shen, Cencheng, et al.
Publicado: (2025)

No Black Box Anymore: Demystifying Clinical Predictive Modeling with Temporal-Feature Cross Attention Mechanism
por: Li, Yubo, et al.
Publicado: (2025)

Explaining Robustness to Catastrophic Forgetting Through Incremental Concept Formation
por: Barari, Nicki, et al.
Publicado: (2025)

Aligning Attention with Human Rationales for Self-Explaining Hate Speech Detection
por: Eilertsen, Brage, et al.
Publicado: (2025)

Learning to Explain: Supervised Token Attribution from Transformer Attention Patterns
por: Mihaila, George
Publicado: (2026)

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
por: Wang, Yiming, et al.
Publicado: (2025)

Feature-to-Image Data Augmentation: Improving Model Feature Extraction with Cluster-Guided Synthetic Samples
por: Haghbin, Yasaman, et al.
Publicado: (2024)

Towards Explaining Deep Neural Network Compression Through a Probabilistic Latent Space
por: Mozafari-Nia, Mahsa, et al.
Publicado: (2024)

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data
por: Wang, Linshanshan, et al.
Publicado: (2025)

Bipartite Graph Attention-based Clustering for Large-scale scRNA-seq Data
por: Liang, Zhuomin, et al.
Publicado: (2026)

FAConvLSTM: Factorized-Attention ConvLSTM for Efficient Feature Extraction in Multivariate Climate Data
por: Nji, Francis Ndikum, et al.
Publicado: (2026)