Prototype-based interpretability methods provide intuitive explanations of
model prediction by comparing samples to a reference set of memorized exemplars
or typical representatives in terms of similarity. In the field of sequential
data modeling, similarity calculations of prototypes are usually based on
encoded representation vectors. However, due to highly recursive functions,
there is usually a non-negligible disparity between the prototype-based
explanations and the original input. In this work, we propose a Self-Explaining
Selective Model (SESM) that uses a linear combination of prototypical concepts
to explain its own predictions. The model employs the idea of case-based
reasoning by selecting sub-sequences of the input that mostly activate
different concepts as prototypical parts, which users can compare to
sub-sequences selected from different example inputs to understand model
decisions. For better interpretability, we design multiple constraints
including diversity, stability, and locality as training objectives. Extensive
experiments in different domains demonstrate that our method exhibits promising
interpretability and competitive accuracy.

本文提出了一种基于样例理解的自选模型，使用原型概念的线性组合来解释自己的预测，以实现更好的可解释性，并通过多种限制条件进行优化。实验结果表明该方法具有良好的可解释性和竞争性的准确性。

学习选择典型部分进行可解释的序列数据建模

Learning to Select Prototypical Parts for Interpretable Sequential Data Modeling

While neural networks have acted as a strong unifying force in the design of
modern AI systems, the neural network architectures themselves remain highly
heterogeneous due to the variety of tasks to be solved. In this chapter, we
explore how to adapt the Layer-wise Relevance Propagation (LRP) technique used
for explaining the predictions of feed-forward networks to the LSTM
architecture used for sequential data modeling and forecasting. The special
accumulators and gated interactions present in the LSTM require both a new
propagation scheme and an extension of the underlying theoretical framework to
deliver faithful explanations.

本章节探究如何使 Layer-wise Relevance Propagation (LRP) 技术适用于序列数据建模和预测所使用的 LSTM 架构，需要更新传播方案并扩展基础理论框架以提供准确的解释。

解释和解读 LSTMs

Explaining and Interpreting LSTMs

Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term
Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved
promising performance in sequential data modeling. The hidden layers in RNNs
can be regarded as the memory units, which are helpful in storing information
in sequential contexts. However, when dealing with high dimensional input data,
such as video and text, the input-to-hidden linear transformation in RNNs
brings high memory usage and huge computational cost. This makes the training
of RNNs unscalable and difficult. To address this challenge, we propose a novel
compact LSTM model, named as TR-LSTM, by utilizing the low-rank tensor ring
decomposition (TRD) to reformulate the input-to-hidden transformation. Compared
with other tensor decomposition methods, TR-LSTM is more stable. In addition,
TR-LSTM can complete an end-to-end training and also provide a fundamental
building block for RNNs in handling large input data. Experiments on real-world
action recognition datasets have demonstrated the promising performance of the
proposed TR-LSTM compared with the tensor train LSTM and other state-of-the-art
competitors.

提出了利用低秩张量环分解重新构建输入到隐藏层变换的紧凑型 LSTM 模型 TR-LSTM，不仅在处理高维输入数据（如视频和文本）方面更具可扩展性和可训练性，而且其良好的稳定性和与其他最先进竞争对手相比的较高性能进一步表明了它在序列数据建模中的潜力。