Skill UI

Automated guidance for hyperparameter tuning within ML training workflows, offering best practices, production-ready code, and validation across modeling stages like data prep, training, and experiment tracking.

scvi-tools is a comprehensive Python framework for probabilistic deep generative models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it enables batch correction, transfer learning, differential expression analysis with uncertainty, and multi-modal data integration. Best for advanced modeling of single-cell RNA-seq, ATAC-seq, CITE-seq, spatial transcriptomics, and specialized modalities like methylation and cytometry.

This skill guides authors on how to strategically position their research for the INTERSPEECH conference. It covers core topics such as speech recognition, synthesis, paralinguistics, and audio-language modeling. Use it to re-frame manuscripts from journal or arXiv styles into a selective CS conference narrative, ensuring the work meets the rigorous standards of audio-specific evaluation, datasets, and error analysis expected by the field.