TRACE: Tractable Routing Autoencoder for Clinical ECG
cs.LG
Submitted: 2026-09-20
Updated: 2026-09-20
Comments: 20 pages, 7 figures, 7 tables
Code: https://github.com/R4nzer/TRACE
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model
- Underspecification Presents Challenges for Credibility in Modern Machine Learning
- Concept Bottleneck Models
- Attention Is All You Need
- CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
- A Simple Framework for Contrastive Learning of Visual Representations
- Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners
- Large-scale Training of Foundation Models for Wearable Biosignals
- Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU
- Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram
- Do Concept Bottleneck Models Learn as Intended?
- Label-Free Concept Bottleneck Models
- Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off
- Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
- ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
- Knowledge-guided Machine Learning: Current Trends and Future Prospects
- Disentangling by Factorising
- Isolating Sources of Disentanglement in Variational Autoencoders
- On Disentangled Representations Learned From Correlated Data
- Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
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