most citedEfficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

2 citations · 2 across the 2 of their papers we have counts for

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2 papers

cs.LG2024★ 2 cited

Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Enshu Liu, Junyi Zhu, Zinan Lin +6

The rapid advancement of large language models (LLMs) has led to architectures with billions to trillions of parameters, posing significant deployment challenges due to their subst…

cs.IR2023

Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset

Dugang Liu, Pengxiang Cheng, Zinan Lin +6

Debiased recommendation with a randomized dataset has shown very promising results in mitigating the system-induced biases. However, it still lacks more theoretical insights or an…