Challenges shared by Richard

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In my experience, simulated data is kinda useless for training, especially when the data is so noisy. Fun blue sky project but I strongly doubt we can build a convincing generative model for the kind of data where a spearman of 3-4% is considered good, clearly our data is barely understood by our models


Depending on how you see it adding noise is a form of augmentation. I think custom noise (swapping / masking / etc.) layers can practically add robustness and performance. Custom layers are way more practical than metric learning or topological data analysis.