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Found 4 papers, 1 papers with code
Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt
But most computation and time is wasted on redundant and noisy points that are already learnt or not learnable.
Mitigating harm in language models with conditional-likelihood filtration
Language models trained on large-scale unfiltered datasets curated from the open web acquire systemic biases, prejudices, and harmful views from their training data.
Prioritized training on points that are learnable, worth learning, and not yet learned (workshop version)
We introduce Goldilocks Selection, a technique for faster model training which selects a sequence of training points that are "just right".
Add a SideNet to your MainNet
As the performance and popularity of deep neural networks has increased, so too has their computational cost.
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