Research map / Graph learning / Graph self-supervised learning
AutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music (ICLR 2017 · similarity 0.88)
- Discovering and Steering Interpretable Concepts in Large Generative Music Models (ICLR 2026 · similarity 0.88)
- Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance (ICML 2019 · similarity 0.88)
- Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models (ICLR 2024 · similarity 0.86)
- From Isolated feature to Orbits:\\Discovering Music Concepts via Multi-SAE Alignment (NeurIPS 2026 · similarity 0.86)
- MusER: Musical Element-Based Regularization for Generating Symbolic Music with Emotion (AAAI 2024 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
