arXiv (All)
open access
Fast Diffusion-GAN Model for Generating Symbolic Music with Emotions
Diffusion models have shown promising results for a wide range of generative tasks with continuous data, such as image and audio synthesis. However, little progress has been made on using diffusion models to generate symbolic music since this class of generative models struggles with discrete data w
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Denoising Diffusion Generative Models Secretly Calculate Attentions#820887
- Accelerating Disaggregated RL for Visual Generative LLMs with Diffusion-Based Parallelism and Trainer-Assisted Generation#303170
- LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model#130738
- EmboAlign: Aligning Video Generation with Compositional Constraints for Zero-Shot Manipulation#1011054
- Bayesian Inversion Beyond the Generator's Range: Reducing Non-physical Constraints from Generative Neural Networks#1328204
Record · ID 1326065
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive