本文的方法SiRA,将 Sparse MOE 和 lora 结合起来,相比 lora 收敛更快。相比 MoLoRA 节约了计算资源。 方法倒是很直接。也考虑到了 MoE 常规会考虑的Token Capacity 和 Auxiliary Loss. 效果的话: 其实也是半斤八俩,略好。 不过作者也说了 倒是挺合适。
Domain adaptation tries to mitigate this degradation. This chapter presents an overview of recent domain adaptation methods based on sparse and low-rank representations.Pattern Recognition and Big Datadoi:10.1142/9789813144552_0004Rama ChellappaVishal M. Patel...
🎉 This is the implementation of EMNLP 2023 paper:Sparse Low-rank Adaptation of Pre-trained Language Models Requirements To run our code, please install all the dependency packages by using the following command: pip install -r requirements.txt ...
Low Rank Adaptation (LoRA) has gained massive attention in the recent generative AI research. One of the main advantages of LoRA is its ability to be fused with pretrained models adding no overhead during inference. However, from a mobile deployment standpoint, we can either avoid inference ...
The sparse constraint can reduce the number of free parameters while the low rank constraint can limit the dimension of phone variation subspace, which are both benefit to the generalization ability. Experimental results show that the proposed method can improve the adaptation performance substantially,...
The proposed method can capture the global mixture of the clustering structure (by the sparseness and low rankness) and the locally consistent structure (by the local graph regularization) as well as the distribution difference (by the distribution adaptation) of the domains data. Hence, the ...
Wang et al. [10] re-cast SIR into a "pseudo" sparse reduced-rank regression problem and showed consistency in central subspace estimation. By con- structing artificial response variables made up from top eigenvectors of the estimated conditional covariance matrix, [11] introduced the ...
In the DEAP experiment subjects were exposed to a set of 1-min long videos and asked to rank the levels of different emotions felt for each video. The emotion categories used were based on Russell’s Valence-Arousal scale48. The emotions of Valence (unpleasant to pleasant), arousal (lack ...
(detection rate), per individual. Spearman’s rank correlation between the binarized profile and the mean counts (across all genes) was ≥ 0.99 (Additional file1: Fig. S12) for every individual, implying that pseudobulk aggregation with binarized expression faithfully represents counts. To ...
We propose an alternating minimization method with iterative hard thresholding -- AMHT-LRS -- to learn the low-rank and sparse part. For the realizable, Gaussian data setting, we show that AMHT-LRS solves the problem efficiently with nearly optimal samples. A significant challenge in ...