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ICLR 2024 — Best Papers & Talks (ImageGen, Vision, Transformers, State Space Models) ft. Durk Kingma, Christian Szegedy, Ilya Sutskever

Latent Space: The AI Engineer Podcast

CHAPTER

Unraveling Unsupervised Learning and Compression

This chapter explores the complex relationship between unsupervised learning and compression, focusing on auto-encoders and generative models. It highlights the potential of unsupervised methods to uncover hidden data structures, while discussing historical challenges and introducing innovative perspectives on distribution matching and effective strategies for utilizing unlabeled data. Additionally, it presents intriguing findings on the capabilities of language models and their surprising applications in image compression.

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