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cs.LG2024
Understanding Self-Supervised Learning via Gaussian Mixture Models
Parikshit Bansal, Ali Kavis, Sujay Sanghavi
Self-supervised learning attempts to learn representations from un-labeled data; it does so via a loss function that encourages the embedding of a point to be close to that of its…
cs.LG2024
Understanding the Training Speedup from Sampling with Approximate Losses
Rudrajit Das, Xi Chen, Bertram Ieong +2
It is well known that selecting samples with large losses/gradients can significantly reduce the number of training steps. However, the selection overhead is often too high to yiel…