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Found 3 papers, 3 papers with code
Rethinking Pruning Large Language Models: Benefits and Pitfalls of Reconstruction Error Minimization
This work suggests fundamentally rethinking the current practice of pruning large language models (LLMs).
Critical Influence of Overparameterization on Sharpness-aware Minimization
Training overparameterized neural networks often yields solutions with varying generalization capabilities, even when achieving similar training losses.
A Closer Look at the Intervention Procedure of Concept Bottleneck Models
Concept bottleneck models (CBMs) are a class of interpretable neural network models that predict the target response of a given input based on its high-level concepts.
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