Search Results for author: Luning Wang

Found 7 papers, 3 papers with code

CSKV: Training-Efficient Channel Shrinking for KV Cache in Long-Context Scenarios

1 code implementation16 Sep 2024 Luning Wang, Shiyao Li, Xuefei Ning, Zhihang Yuan, Shengen Yan, Guohao Dai, Yu Wang

Therefore, we introduce CSKV, a training-efficient Channel Shrinking technique for KV cache compression: (1) We first analyze the singular value distribution of the KV cache, revealing significant redundancy and compression potential along the channel dimension.

Joint Universal Adversarial Perturbations with Interpretations

no code implementations3 Aug 2024 Liang-bo Ning, Zeyu Dai, Wenqi Fan, Jingran Su, Chao Pan, Luning Wang, Qing Li

Recent studies have shown that adversaries can manipulate the predictions of DNNs by adding a universal adversarial perturbation (UAP) to benign samples.

Evaluating Quantized Large Language Models

1 code implementation28 Feb 2024 Shiyao Li, Xuefei Ning, Luning Wang, Tengxuan Liu, Xiangsheng Shi, Shengen Yan, Guohao Dai, Huazhong Yang, Yu Wang

Specifically, PTQ can effectively mitigate memory consumption and reduce computational overhead in LLMs.

Mamba Quantization

A Causal Framework to Unify Common Domain Generalization Approaches

no code implementations13 Jul 2023 Nevin L. Zhang, Kaican Li, Han Gao, Weiyan Xie, Zhi Lin, Zhenguo Li, Luning Wang, Yongxiang Huang

Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s).

Domain Generalization

Model Debiasing via Gradient-based Explanation on Representation

no code implementations20 May 2023 Jindi Zhang, Luning Wang, Dan Su, Yongxiang Huang, Caleb Chen Cao, Lei Chen

Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem.

Disentanglement Fairness +1

Consistency Regularization for Domain Generalization with Logit Attribution Matching

1 code implementation13 May 2023 Han Gao, Kaican Li, Weiyan Xie, Zhi Lin, Yongxiang Huang, Luning Wang, Caleb Chen Cao, Nevin L. Zhang

In this paper, we consider a third, lesser-known setting where a training domain is endowed with a collection of pairs of examples that share the same semantic information.

Data Augmentation Domain Generalization

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