BSI ranks singular-vector bases for LLM low-rank compression by estimating expected task loss increase via second-order Taylor expansion of the loss and an efficient Hessian-diagonal estimator, outperforming magnitude-based baselines on math reasoning benchmarks.
Accelerating Very Deep Convolutional Networks for Classification and Detection.IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 38(10):1943–1955, 2016
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Importance-Guided Basis Selection for Low-Rank Decomposition of Large Language Models
BSI ranks singular-vector bases for LLM low-rank compression by estimating expected task loss increase via second-order Taylor expansion of the loss and an efficient Hessian-diagonal estimator, outperforming magnitude-based baselines on math reasoning benchmarks.