Recently, the connectionist temporal classification (CTC) model coupled with
recurrent (RNN) or convolutional neural networks (CNN), made it easier to train
speech recognition systems in an end-to-end fashion. However in real-valued
models, time frame components such as mel-filter-bank energies and the cepstral
coefficients obtained from them, together with their first and second order
derivatives, are processed as individual elements, while a natural alternative
is to process such components as composed entities. We propose to group such
elements in the form of quaternions and to process these quaternions using the
established quaternion algebra. Quaternion numbers and quaternion neural
networks have shown their efficiency to process multidimensional inputs as
entities, to encode internal dependencies, and to solve many tasks with less
learning parameters than real-valued models. This paper proposes to integrate
multiple feature views in quaternion-valued convolutional neural network
(QCNN), to be used for sequence-to-sequence mapping with the CTC model.
Promising results are reported using simple QCNNs in phoneme recognition
experiments with the TIMIT corpus. More precisely, QCNNs obtain a lower phoneme
error rate (PER) with less learning parameters than a competing model based on
real-valued CNNs.

该研究论文介绍了一种新的方法，使用四元数代替传统的时间帧维度元素作为输入特征来训练基于 QCNN 和 CTC 模型进行序列到序列映射的语音识别系统，实验结果表明，相较于基于传统卷积神经网络的竞争模型，该方法具有更高的准确率且参数更少。

四元数卷积神经网络用于端到端自动语音识别

Quaternion Convolutional Neural Networks for End-to-End Automatic Speech  Recognition

We describe a fully data driven model that learns to perform a retrosynthetic
reaction prediction task, which is treated as a sequence-to-sequence mapping
problem. The end-to-end trained model has an encoder-decoder architecture that
consists of two recurrent neural networks, which has previously shown great
success in solving other sequence-to-sequence prediction tasks such as machine
translation. The model is trained on 50,000 experimental reaction examples from
the United States patent literature, which span 10 broad reaction types that
are commonly used by medicinal chemists. We find that our model performs
comparably with a rule-based expert system baseline model, and also overcomes
certain limitations associated with rule-based expert systems and with any
machine learning approach that contains a rule-based expert system component.
Our model provides an important first step towards solving the challenging
problem of computational retrosynthetic analysis.

本论文讲述了一种完全基于数据驱动的模型，该模型学习执行一个反合成反应预测任务，将其作为序列到序列映射问题来处理。该模型为端到端的训练模型，具有编码器 - 解码器结构，包含两个递归神经网络，该模型通过对美国专利文献中的 50,000 个实验反应示例进行训练，拥有解决计算机反应分析的挑战性问题的重要前进一步。

神经序列到序列模型在反合成反应预测中的应用

Retrosynthetic reaction prediction using neural sequence-to-sequence  models

In the domain of sequence modelling, Recurrent Neural Networks (RNN) have
been capable of achieving impressive results in a variety of application areas
including visual question answering, part-of-speech tagging and machine
translation. However this success in modelling short term dependencies has not
successfully transitioned to application areas such as trajectory prediction,
which require capturing both short term and long term relationships. In this
paper, we propose a Tree Memory Network (TMN) for modelling long term and short
term relationships in sequence-to-sequence mapping problems. The proposed
network architecture is composed of an input module, controller and a memory
module. In contrast to related literature, which models the memory as a
sequence of historical states, we model the memory as a recursive tree
structure. This structure more effectively captures temporal dependencies
across both short term and long term sequences using its hierarchical
structure. We demonstrate the effectiveness and flexibility of the proposed TMN
in two practical problems, aircraft trajectory modelling and pedestrian
trajectory modelling in a surveillance setting, and in both cases we outperform
the current state-of-the-art. Furthermore, we perform an in depth analysis on
the evolution of the memory module content over time and provide visual
evidence on how the proposed TMN is able to map both long term and short term
relationships efficiently via a hierarchical structure.

提出了一种基于树形记忆网络的序列映射模型，用于捕获长期和短期依赖关系，并在飞行轨迹建模和行人轨迹建模等实际问题中实现了较好的性能。