Learning Latent Memory States from Longitudinal Athlete Monitoring Data

arXiv:2608.06290 · stat.CO, stat.ME, stat.ML · Submitted 2026-08-06 · Read on arXiv

Dae-Jin Lee

stat.CO, stat.ME, stat.ML

Submitted: 2026-08-06

Code: https://github.com/idaejin/latent-memory-states

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

Terminology

• Transformer — 21× in this paper · explained in 2 episodes
• Accuracy — 20× in this paper · explained in 1 episode
• Personalization — 16× in this paper · explained in 2 episodes
• Interpretability — 9× in this paper · explained in 15 episodes
• Longitudinal Data — 8× in this paper · explained in 2 episodes
• Clustering — 6× in this paper · explained in 2 episodes
• Coherence — 6× in this paper · explained in 2 episodes
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Hyperparameters — 3× in this paper · explained in 2 episodes
• Representation Learning — 3× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 6 episodes
• Sufficiency — 2× in this paper · explained in 2 episodes
• Transformers — 2× in this paper · explained in 2 episodes
• Weight Decay — 2× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 9 episodes
• Decoder — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Global Positioning System — 1× in this paper
• Information Bottleneck — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Transformer Encoder — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 9 episodes

Sources

• SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal data
• The information bottleneck method

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