Operator Boosting constructs compact neural-operator PDE surrogates by sequential residual learning with validation-selected shrinkage, yielding 72-95% parameter reduction and accuracy gains on 21 of 30 dataset-architecture pairs.
InAdvances in Neural Information Processing Systems, Vol
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MixT compresses Transformer LLMs by substituting targeted linear projections with tensor-operator mixtures, preserving MMLU accuracy up to model-specific boundaries where parameter count drops 47.5% and inference memory 60.4% on LLaMA2-7B.
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Operator Boosting Produces Pareto-Efficient PDE Surrogates
Operator Boosting constructs compact neural-operator PDE surrogates by sequential residual learning with validation-selected shrinkage, yielding 72-95% parameter reduction and accuracy gains on 21 of 30 dataset-architecture pairs.
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A general tensor-structured compression scheme for efficient large language models
MixT compresses Transformer LLMs by substituting targeted linear projections with tensor-operator mixtures, preserving MMLU accuracy up to model-specific boundaries where parameter count drops 47.5% and inference memory 60.4% on LLaMA2-7B.