This is a further development of Vision Transformer Pruning via matrix
decomposition. The purpose of the Vision Transformer Pruning is to prune the
dimension of the linear projection of the dataset by learning their associated
importance score in order to reduce the storage, run-time memory, and
computational demands. In this paper we further reduce dimension and complexity
of the linear projection by implementing and comparing several matrix
decomposition methods while preserving the generated important features. We end
up selected the Singular Value Decomposition as the method to achieve our goal
by comparing the original accuracy scores in the original Github repository and
the accuracy scores of using those matrix decomposition methods, including
Singular Value Decomposition, four versions of QR Decomposition, and LU
factorization.

通过使用矩阵分解实现视觉转换器修剪，该论文进一步在保留重要特征的基础上，比较了多种矩阵分解方法，最终选择奇异值分解作为降维和计算复杂度减少的方法，通过与原准确率得分进行比较实现目标。

通过矩阵分解的视觉 Transformer 剪枝

Vision Transformer Pruning Via Matrix Decomposition

In this paper, we propose a self-attentive bidirectional long short-term
memory (SA-BiLSTM) network to predict multiple emotions for the EmotionX
challenge. The BiLSTM exhibits the power of modeling the word dependencies, and
extracting the most relevant features for emotion classification. Building on
top of BiLSTM, the self-attentive network can model the contextual dependencies
between utterances which are helpful for classifying the ambiguous emotions. We
achieve 59.6 and 55.0 unweighted accuracy scores in the \textit{Friends} and
the \textit{EmotionPush} test sets, respectively.

提出了 SA-BiLSTM 网络，用于 EmotionX 挑战中多情感预测，其联合运用双向 LSTM 和自我注意力网络实现对话语境下模糊情感的分类，测试集中在 Friends 和 EmotionPush 数据集上分别获得 59.6 和 55.0 的不加权准确度。