Cover Image

Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cui, Jin, Capstick, Alexander, Barnaghi, Payam, Scott, Gregory
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia
by: Cui, Jin, et al.
Published: (2025)

Representation Learning of Daily Movement Data Using Text Encoders
by: Capstick, Alexander, et al.
Published: (2024)

Enabling Regional Explainability by Automatic and Model-agnostic Rule Extraction
by: Chen, Yu, et al.
Published: (2024)

AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling
by: Capstick, Alexander, et al.
Published: (2024)

Training Neural Networks on Data Sources with Unknown Reliability
by: Capstick, Alexander, et al.
Published: (2022)

Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity
by: Fan, Kexin, et al.
Published: (2025)

DynamicLight: Two-Stage Dynamic Traffic Signal Timing
by: Zhang, Liang, et al.
Published: (2022)

Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment
by: Lima, Maria R., et al.
Published: (2025)

The Debate on RLVR Reasoning Capability Boundary: Shrinkage, Expansion, or Both? A Two-Stage Dynamic View
by: Yao, Xinhao, et al.
Published: (2025)

Hierarchical Mixture-of-Experts with Two-Stage Optimization
by: Molodtsov, Gleb, et al.
Published: (2026)

Graph Neural Networks in Multi-Omics Cancer Research: A Structured Survey
by: Zohari, Payam, et al.
Published: (2025)

Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler
by: Dremov, Aleksandr, et al.
Published: (2025)

Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
by: Jajoo, Pranaya, et al.
Published: (2026)

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition
by: Lu, Yao, et al.
Published: (2025)

Federated Two Stage Decoupling With Adaptive Personalization Layers
by: Zhu, Hangyu, et al.
Published: (2023)

Two-Stage Learned Decomposition for Scalable Routing on Multigraphs
by: Rydin, Filip, et al.
Published: (2026)

Individualized and Interpretable Sleep Forecasting via a Two-Stage Adaptive Spatial-Temporal Model
by: Wang, Xueyi, et al.
Published: (2025)

Two-Stage Grid Optimization for Group-wise Quantization of LLMs
by: Kim, Junhan, et al.
Published: (2026)

Two-Stage Regularization-Based Structured Pruning for LLMs
by: Feng, Mingkuan, et al.
Published: (2025)

TAP: Two-Stage Adaptive Personalization of Multi-Task and Multi-Modal Foundation Models in Federated Learning
by: Lee, Seohyun, et al.
Published: (2025)

FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning
by: Fan, Boyu, et al.
Published: (2024)

Characterizing and Predicting Wildfire Evacuation Behavior: A Dual-Stage ML Approach
by: Polock, Sazzad Bin Bashar, et al.
Published: (2026)

TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributions
by: YuHang, Wang, et al.
Published: (2025)

Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty
by: Lin, Tianjue, et al.
Published: (2026)

From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks
by: Salihovic, Allan, et al.
Published: (2025)

Disentangling Feature Structure: A Mathematically Provable Two-Stage Training Dynamics in Transformers
by: Gong, Zixuan, et al.
Published: (2025)

Quriosity: Analyzing Human Questioning Behavior and Causal Inquiry through Curiosity-Driven Queries
by: Ceraolo, Roberto, et al.
Published: (2024)

From Navigation to Refinement: Revealing the Two-Stage Nature of Flow-based Diffusion Models through Oracle Velocity
by: Liu, Haoming, et al.
Published: (2025)

Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning
by: Tong, Anh, et al.
Published: (2025)

A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanations
by: Artelt, André, et al.
Published: (2024)

Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression
by: Liang, Haodong, et al.
Published: (2025)

Enhancing Inverse Reinforcement Learning through Encoding Dynamic Information in Reward Shaping
by: Zhan, Simon Sinong, et al.
Published: (2024)

Hadamax Encoding: Elevating Performance in Model-Free Atari
by: Kooi, Jacob E., et al.
Published: (2025)

The Energy Cost of Reasoning: Analyzing Energy Usage in LLMs with Test-time Compute
by: Jin, Yunho, et al.
Published: (2025)

HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming
by: Wu, Yang, et al.
Published: (2025)

Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning
by: Qiu, Haomiao, et al.
Published: (2025)

Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks
by: Niu, Peizhi, et al.
Published: (2024)

Fast Adaptation with Behavioral Foundation Models
by: Sikchi, Harshit, et al.
Published: (2025)

From Bias to Behavior: Learning Bull-Bear Market Dynamics with Contrastive Modeling
by: Luo, Xiaotong, et al.
Published: (2025)

Analyzing Memorization in Large Language Models through the Lens of Model Attribution
by: Menta, Tarun Ram, et al.
Published: (2025)