Machine Learning Reading Group

	Paper	Authors	Year	Reference	Links	Suggested by	Key takeaways
#1	I-JEPA Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture	Mahmoud Assran et al.	2023	arXiv:2301.08243	Code Slides	Orian Sharoni & Luke Dzwonczyk	View key takeaways Core JEPA idea: learn representations by predicting the latent representation of a target region from a context region, rather than reconstructing pixels. This provides a foundation for non-generative self-supervised learning and subsequent JEPA models.
#1	Music-JEPA Learning a World Model of Sound from Action	Ziyu Wang et al.	2026	arXiv:2607.22000	Demo Slides	Orian Sharoni & Luke Dzwonczyk	View key takeaways JEPA applied to music/audio: models music as an action-conditioned dynamical system. Audio is treated as the state and piano-roll information as the action, with applications including beat tracking, composer identification, key estimation, and piano transcription.
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#3	ImprovID Machine learning of artistic fingerprints in jazz	Huw Cheston, Reuben Bance & Peter M. C. Harrison	2026	Nature Machine Intelligence 8, 1261–1274	Code Demo	Pierre Sant-Germier	View key takeaways Artistic fingerprint / style identification: uses machine learning to identify individual jazz pianists from their performances, capturing stylistic characteristics across melodic, harmonic, rhythmic, and dynamic dimensions.