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Interested in representation learning? Want to understand your time-series data? Want a method that outperforms tSNE and UMAP in terms of finding consistent latent factors in time-series data? Got your attention? 🔥#cebraAI 1/n
We’ve developed a new (patent pending) method called CEBRA for you. It’s a novel method for supervised contrastive learning with continuous and discrete labels that adapts the sampling process based on label information.
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We show that contrastive learning outperforms generative model-based methods when it comes to consistency/identifying ground truth latents on real world datasets! 3/n
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