FROM Amazon research Germany PROBLEM probabilistic forecasting: estimate the probability distribution of a time series in future. INTRODUCTION a global model, which learns from historical data of all time series. METHOD an autoregressive recurrent ne…

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling 2018-05-16 16:09:15 Introduction: 本文提出一种 TCN (Temporal Convolutional Networks) 网络结构,用卷积的方式进行序列数据的处理,并且取得了和更加复杂的 RNN.LSTM.GRU 等模型相当的精度. Temporal Convolutiona…

新手教程之:循环网络和LSTM指南 (A Beginner’s Guide to Recurrent Networks and LSTMs) 本文翻译自:http://deeplearning4j.org/lstm.html 其他相关教程: 1. 深度神经网络简介 http://deeplearning4j.org/zh-neuralnet-overview 2. 卷积网络 http://deeplearning4j.org/zh-convolutionalnets 目录: 1. 前向传播网络…

From google institution; 1. Before this, DNN cannot be used to map sequences to sequences. In this paper, we propose a sequence learning that makes minimal assumptions on the sequence structure. use lstm to map the input sequence to a vector of a fix…

2017 NIPS, time series workshop traditional methods: ARIMA. Seq2Seq quantile forecast; RELATED WORK DeepAR, probabilistic forecasting with encoder-decoder models. A seq2seq architecture with an identical encoder and decoder. METHOD 为什么要用encoder-decod…

Problem: multi-horizon probabilistic forecasting tasks; Propose an end-to-end framework for multi-horizon time series forecasting, with temporal attention mechanisms to capture latent patterns. Introduction: forecasting ----- understanding demands. t…

由于本章过长,分为两个部分,这是第一部分. 这几年提到RNN,一般指Recurrent Neural Networks,至于翻译成循环神经网络还是递归神经网络都可以.wiki上面把Recurrent Neural Networks叫做时间递归神经网络,与之对应的还有一个结构递归神经网络(recursive neural network).本文讨论的是前者. RNN是一种可以预测未来(在某种程度上)的神经网络,可以用来分析时间序列数据(比如分析股价,预测买入点和卖出点).在自动驾驶中,可以预测路线…

http://karpathy.github.io/2015/05/21/rnn-effectiveness/ There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first…

出处:arXiv: Artificial Intelligence, 2016(一年了还没中吗?) Motivation 使用GAN+RNN来处理continuous sequential data,并训练生成古典音乐 Introduction In this work, we investigate the feasibility of using adversarial training for a sequential model with continuous data, and eva…

目录 1. 网络资源 2. 简介 3. 自适应运算时间 3.1 有限运算时间 3.2 误差梯度 1. 网络资源 这篇文章的写作太随意了,读起来不是很好懂(掺杂了过多的技术细节).因此有作者介绍会更好. B站有视频:https://www.bilibili.com/video/av66005226/ 动机:RNN(LSTM,GRU等)在处理一句话时,对每一个token的计算量是相同的.然而事实上每一个token的重要性不一. 因此,本文考虑在另一个维度:在每个cell的内部,进行差异化改造. 2.…