In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.

In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.

Common to all different kinds of recurrent neural networks (RNNs) is the intention to model relations between data points through time. When there is no immediate relationship between subsequent data points (like when the data points are generated at random, e.g.), we show that RNNs are still able to remember a few data points back into the sequence by memorizing them by heart using standard backpropagation. However, we also show that for classical RNNs, LSTM and GRU networks the distance of data points between recurrent calls that can be reproduced this way is highly limited (compared to even a loose connection between data points) and subject to various constraints imposed by the type and size of the RNN in question. This implies the existence of a hard limit (way below the information-theoretic one) for the distance between related data points within which RNNs are still able to recognize said relation.

当前信息时代在互联网上产生的数据的指数增长是数字经济的推动力。信息提取是累积大数据中的主要价值。对统计分析和手工设计的规则机器学习算法的大数据依赖性被人类语言固有的巨大复杂性所淹没。自然语言处理（NLP）正在装备机器，以了解这些人类多样化和复杂的语言。文本分类是一个NLP任务，它会自动识别基于预定义或未定标记的集合的模式。常见的文本分类应用程序包括信息检索，建模新闻主题，主题提取，情感分析和垃圾邮件检测。在文本中，某些单词序列取决于上一个或下一个单词序列以使其充分含义。这是一项具有挑战性的依赖性任务，要求机器能够存储一些以前的重要信息以影响未来的含义。诸如RNN，GRU和LSTM之类的序列模型是具有长期依赖性任务的突破。因此，我们将这些模型应用于二进制和多类分类。产生的结果非常出色，大多数模型在80％和94％的范围内执行。但是，这个结果并不详尽，因为我们认为如果机器要与人类竞争，可以改进。

近年来，使用正交矩阵已被证明是通过训练，稳定性和收敛尤其是控制梯度来改善复发性神经网络（RNN）的一种有希望的方法。通过使用各种门和记忆单元，封闭的复发单元（GRU）和长期短期记忆（LSTM）体系结构解决了消失的梯度问题，但它们仍然容易出现爆炸梯度问题。在这项工作中，我们分析了GRU中的梯度，并提出了正交矩阵的使用，以防止梯度问题爆炸并增强长期记忆。我们研究了在哪里使用正交矩阵，并提出了基于Neumann系列的缩放尺度的Cayley转换，以训练GRU中的正交矩阵，我们称之为Neumann-cayley Orthoconal orthoconal Gru或简单的NC-GRU。我们介绍了有关几个合成和现实世界任务的模型的详细实验，这些实验表明NC-GRU明显优于GRU以及其他几个RNN。

The time-series forecasting (TSF) problem is a traditional problem in the field of artificial intelligence. Models such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and GRU (Gate Recurrent Units) have contributed to improving the predictive accuracy of TSF. Furthermore, model structures have been proposed to combine time-series decomposition methods, such as seasonal-trend decomposition using Loess (STL) to ensure improved predictive accuracy. However, because this approach is learned in an independent model for each component, it cannot learn the relationships between time-series components. In this study, we propose a new neural architecture called a correlation recurrent unit (CRU) that can perform time series decomposition within a neural cell and learn correlations (autocorrelation and correlation) between each decomposition component. The proposed neural architecture was evaluated through comparative experiments with previous studies using five univariate time-series datasets and four multivariate time-series data. The results showed that long- and short-term predictive performance was improved by more than 10%. The experimental results show that the proposed CRU is an excellent method for TSF problems compared to other neural architectures.

Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for a variety of machine learning problems. This has led to a renewed interest in understanding the role and utility of various computational components of typical LSTM variants. In this paper, we present the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling. The hyperparameters of all LSTM variants for each task were optimized separately using random search, and their importance was assessed using the powerful fANOVA framework. In total, we summarize the results of 5400 experimental runs (≈ 15 years of CPU time), which makes our study the largest of its kind on LSTM networks. Our results show that none of the variants can improve upon the standard LSTM architecture significantly, and demonstrate the forget gate and the output activation function to be its most critical components. We further observe that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment.

我们为复发性神经网络提出了一种新颖的记忆增强机制，该机制利用了人类认知评估在顺序评估任务中的影响。我们将增强记忆的机制概念化为增强记忆单元（RMU），其中包含一个评估状态以及两个正面和负面的增强记忆。通过更强的刺激，这两个增强记忆是腐烂或增强的。此后，评估国家通过积极和消极的强化记忆进行更新。因此，RMU可以在刺激的暴力变化下学习评估差异，以估计人类情感体验。如视频质量评估和经验任务的视频质量的实验所示，拟议的增强记忆单元在复发性神经网络中达到了卓越的性能，这证明了RMU在建模人类认知评估中的有效性。

With the evolution of power systems as it is becoming more intelligent and interactive system while increasing in flexibility with a larger penetration of renewable energy sources, demand prediction on a short-term resolution will inevitably become more and more crucial in designing and managing the future grid, especially when it comes to an individual household level. Projecting the demand for electricity for a single energy user, as opposed to the aggregated power consumption of residential load on a wide scale, is difficult because of a considerable number of volatile and uncertain factors. This paper proposes a customized GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) architecture to address this challenging problem. LSTM and GRU are comparatively newer and among the most well-adopted deep learning approaches. The electricity consumption datasets were obtained from individual household smart meters. The comparison shows that the LSTM model performs better for home-level forecasting than alternative prediction techniques-GRU in this case. To compare the NN-based models with contrast to the conventional statistical technique-based model, ARIMA based model was also developed and benchmarked with LSTM and GRU model outcomes in this study to show the performance of the proposed model on the collected time series data.

Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and encode a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.

预测基金绩效对投资者和基金经理都是有益的，但这是一项艰巨的任务。在本文中，我们测试了深度学习模型是否比传统统计技术更准确地预测基金绩效。基金绩效通常通过Sharpe比率进行评估，该比例代表了风险调整的绩效，以确保基金之间有意义的可比性。我们根据每月收益率数据序列数据计算了年度夏普比率，该数据的时间序列数据为600多个投资于美国上市大型股票的开放式共同基金投资。我们发现，经过现代贝叶斯优化训练的长期短期记忆（LSTM）和封闭式复发单元（GRUS）深度学习方法比传统统计量相比，预测基金的Sharpe比率更高。结合了LSTM和GRU的预测的合奏方法，可以实现所有模型的最佳性能。有证据表明，深度学习和结合能提供有希望的解决方案，以应对基金绩效预测的挑战。

Recent developments in quantum computing and machine learning have propelled the interdisciplinary study of quantum machine learning. Sequential modeling is an important task with high scientific and commercial value. Existing VQC or QNN-based methods require significant computational resources to perform the gradient-based optimization of a larger number of quantum circuit parameters. The major drawback is that such quantum gradient calculation requires a large amount of circuit evaluation, posing challenges in current near-term quantum hardware and simulation software. In this work, we approach sequential modeling by applying a reservoir computing (RC) framework to quantum recurrent neural networks (QRNN-RC) that are based on classical RNN, LSTM and GRU. The main idea to this RC approach is that the QRNN with randomly initialized weights is treated as a dynamical system and only the final classical linear layer is trained. Our numerical simulations show that the QRNN-RC can reach results comparable to fully trained QRNN models for several function approximation and time series prediction tasks. Since the QRNN training complexity is significantly reduced, the proposed model trains notably faster. In this work we also compare to corresponding classical RNN-based RC implementations and show that the quantum version learns faster by requiring fewer training epochs in most cases. Our results demonstrate a new possibility to utilize quantum neural network for sequential modeling with greater quantum hardware efficiency, an important design consideration for noisy intermediate-scale quantum (NISQ) computers.

在本文中，我们提供了一种系统的方法来评估和比较数字信号处理中神经网络层的计算复杂性。我们提供并链接四个软件到硬件的复杂性度量，定义了不同的复杂度指标与层的超参数的关系。本文解释了如何计算这四个指标以进行馈送和经常性层，并定义在这种情况下，我们应该根据我们是否表征了面向更软件或硬件的应用程序来使用特定的度量。新引入的四个指标之一，称为“添加和位移位数（NAB）”，用于异质量化。 NABS不仅表征了操作中使用的位宽的影响，还表征了算术操作中使用的量化类型。我们打算这项工作作为与神经网络在实时数字信号处理中应用相关的复杂性估计级别（目的）的基线，旨在统一计算复杂性估计。

具有诸如LSTM或GRU之类的门控机制的经常性神经网络是模拟顺序数据的强大工具。在机制中，最近被引入到RNN中的隐藏状态下控制信息流的忘记门被重新解释为状态的时间尺度的代表，即RNN保留信息的时间在输入上。在此解释的基础上，已经提出了几种参数初始化方法，以利用数据依赖于数据中的时间依赖性的知识，以提高可读性。然而，解释依赖于各种不切实际的假设，例如在一定时间点之后没有输入。在这项工作中，我们重新考虑了忘记门的解释，更现实的环境。我们首先概括了所存在的网格RNN理论，以便我们可以考虑连续给出输入的情况。然后，我们争论作为时间表示的忘记门的解释是有效的，当随着时间的推移时，当相对于国家的损失的梯度减小时是有效的。我们经验证明现有的RNNS在初始训练阶段满足了几个任务的初始训练阶段，这与先前的初始化方法很好。在此发现的基础上，我们提出了一种构建新的RNN的方法，可以代表比传统模型更长的时间级，这将提高长期顺序数据的可读性。通过使用现实世界数据集的实验，我们验证了我们的方法的有效性。

We introduce a novel gated recurrent unit (GRU) with a weighted time-delay feedback mechanism in order to improve the modeling of long-term dependencies in sequential data. This model is a discretized version of a continuous-time formulation of a recurrent unit, where the dynamics are governed by delay differential equations (DDEs). By considering a suitable time-discretization scheme, we propose $\tau$-GRU, a discrete-time gated recurrent unit with delay. We prove the existence and uniqueness of solutions for the continuous-time model, and we demonstrate that the proposed feedback mechanism can help improve the modeling of long-term dependencies. Our empirical results show that $\tau$-GRU can converge faster and generalize better than state-of-the-art recurrent units and gated recurrent architectures on a range of tasks, including time-series classification, human activity recognition, and speech recognition.

Recurrent neural networks are a widely used class of neural architectures. They have, however, two shortcomings. First, they are often treated as black-box models and as such it is difficult to understand what exactly they learn as well as how they arrive at a particular prediction. Second, they tend to work poorly on sequences requiring long-term memorization, despite having this capacity in principle. We aim to address both shortcomings with a class of recurrent networks that use a stochastic state transition mechanism between cell applications. This mechanism, which we term state-regularization, makes RNNs transition between a finite set of learnable states. We evaluate state-regularized RNNs on (1) regular languages for the purpose of automata extraction; (2) non-regular languages such as balanced parentheses and palindromes where external memory is required; and (3) real-word sequence learning tasks for sentiment analysis, visual object recognition and text categorisation. We show that state-regularization (a) simplifies the extraction of finite state automata that display an RNN's state transition dynamic; (b) forces RNNs to operate more like automata with external memory and less like finite state machines, which potentiality leads to a more structural memory; (c) leads to better interpretability and explainability of RNNs by leveraging the probabilistic finite state transition mechanism over time steps.

Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.

Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation. In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder-Decoder and a newly proposed gated recursive convolutional neural network. We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase. Furthermore, we find that the proposed gated recursive convolutional network learns a grammatical structure of a sentence automatically.

研究了自闭症数据集，以确定自闭症和健康组之间的差异。为此，分析了这两组的静止状态功能磁共振成像（RS-FMRI）数据，并创建了大脑区域之间的连接网络。开发了几个分类框架，以区分组之间的连接模式。比较了统计推断和精度的最佳模型，并分析了精度和模型解释性之间的权衡。最后，据报道，分类精度措施证明了我们框架的性能。我们的最佳模型可以以71％的精度将自闭症和健康的患者分类为多站点I数据。

In this paper, we explore the inclusion of latent random variables into the hidden state of a recurrent neural network (RNN) by combining the elements of the variational autoencoder. We argue that through the use of high-level latent random variables, the variational RNN (VRNN) 1 can model the kind of variability observed in highly structured sequential data such as natural speech. We empirically evaluate the proposed model against other related sequential models on four speech datasets and one handwriting dataset. Our results show the important roles that latent random variables can play in the RNN dynamics.