introduction to recurrent neural networks (rnn), long short-term memory (lstm) wenjie pei

Introduction to Recurrent neural networks (RNN), Long short-term memory (LSTM) Wenjie Pei

Upload: mae-booker

Post on 21-Dec-2015

233 views

Category:

Documents

0 download

Report

Download

Embed Size (px):

TRANSCRIPT

Introduction to Recurrent neural networks (RNN), Long short-

term memory (LSTM)

Wenjie Pei

Artificial Neural Networks

• Feedforward neural networks– ANNs without cycle connections between nodes

• (Feedback) Recurrent neural networks– ANNs with cycle connections between nodes

Page 3: Introduction to Recurrent neural networks (RNN), Long short-term memory (LSTM) Wenjie Pei

Feedforward Neural Networks

• Multilayer perceptron (MLP)

Universal function approximation theory:Sufficient nonlinear hidden units approximate any continuous mapping function

Drawback:Output depends only on the current inputNo temporal information dependencies

Recurrent Neural Networks

Advantage: Memory of previous inputsIncorporate contextual information

Feedback from hidden unit activation of last time step to current time step

Universal approximation theory:An RNN with sufficient hidden unitsAny measurable sequence-to-sequence mappingor dynamic system

Recurrent Neural Networks

• Bidirectional RNNs

Recurrent Neural Networks

• Vanishing gradient problem

Sensitivity decay exponentially over the time

Page 7: Introduction to Recurrent neural networks (RNN), Long short-term memory (LSTM) Wenjie Pei

Long Short-Term Memory (LSTM)Input gate [0, 1]: How much information from input could go into the cell

Forget gate [0, 1]: How much information from last time step could enter the cell

Output gate [0, 1]: How much information to output

Long Short-Term Memory

• Advantage: long-period time memory

Applications

• Applications to sequence labeling problems:– Handwritten character recognition– Speech recognition– Protein secondary structure prediction– …

Want to know more about the latest papers:Waiting for my next coffee talk

Recurrent Neural Networks · Recurrent Neural Networks 11-785 / 2020 Spring / Recitation 7 Vedant Sanil, David Park “Drop your RNN and LSTM, they are no good!” The fall of RNN

EcoRNN: Efficient Computing of LSTM RNN on GPUs · Figure 1: Left: A single-layer LSTM RNN that scans through an input sequence. Right: A zoom-in view of an LSTM cell. Both diagrams

Supervised Sequence Labelling with Recurrent Neural Networksgraves/phd.pdfLong Short-Term Memory (LSTM) is an RNN architecture speci cally designed to overcome this limi-tation. In

RNN LSTM and Deep Learning Libraries · RNN LSTM and Deep Learning Libraries UDRC Summer School Muhammad Awais [email protected]

AI602 Lec04 RNN architectures(모상우)alinlab.kaist.ac.kr/resource/AI602_Lec04_RNN... · 2019. 9. 17. · Algorithmic Intelligence Lab •Long Short-Term Memory (LSTM) •A special

Recurrent Neural Networks · 2020-04-06 · Overview •Finite state models •Recurrent neural networks (RNNs) •Training RNNs •RNN Models •Long short-term memory (LSTM) •Attention

Fast, Compact, and High Quality LSTM-RNN Based … · Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices Heiga Zen, Yannis

Automated Neural Image Caption Generator for Visually Impaired …cs224d.stanford.edu/reports/mcelamri.pdf · 2016-06-20 · then fed into a vanilla RNN or a LSTM. The vanilla RNN

NILC-SWORNEMO at the Surface Realization Shared Task ... · work (RNN) that we used was the Long Short-Term Memory (LSTM). We used a Bidirectional LSTM (Bi-LSTM) in the Encoder because

180912 Lec6 RNN architectures(LSTM, GRU 등 최신 RNN 모델, …alinlab.kaist.ac.kr/resource/Lec6_RNN_architectures.pdf• Google’s Neural Machine Translation (GNMT) 3. Overcoming

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811511.pdf · (RNN, LSTM) to predict the direction of price movement (up or down) of the Dow Jones Industrial Average

River Flood Prediction Using a Long Short- Term Memory ... · Long short-term memory (LSTM) model Type of Recurrent Neural Network (RNN) Composed of several, connected networks which

Automated Sleep Apnea Detection in Raw Respiratory Signals ... · Long Short-Term Memory (LSTM) neural networks, a type of Recurrent Neural Network (RNN), were proposed as a good

arXiv:1512.05287v5 [stat.ML] 5 Oct 2016 · insights into the use of the technique with RNN models. Here we focus on common RNN models in the ﬁeld (LSTM [18], GRU [19]) and interpret

Fast Signature Scheme for Network Coding Mingxi Yang, Wenjie Yan Reporter: Wenjie Yan Mingxi Yang, Wenjie Yan1 DCABES 2009

COMPARISON OF RNN, LSTM AND GRU ON SPEECH … · 2020. 1. 24. · class of RNN, Long Short-Term Memory [LSTM] networks. LSTM networks have special memory cell structure, which is

Deep Learning: Models for Sequence Data (RNN and LSTM) · Recap: Convolutional Neural Network Special type of feedforward neural nets (local connectivity + weight sharing) Each layer

Pixel RNN/CNN for Generative Image Modelpr-ai.hit.edu.cn/_upload/article/files/df/3a/bbe2530444669b9bed4ea... · Approach: Spatial LSTM, PixelRNN / PixelCNN [1] Generative image modeling

Neural Networks Convolutional and Recurrentai-nlp-ml/course/dnlp/CNN-RNN-LSTM-Attenti… · Convolutional Neural Networks (CNN) A CNN consists of a series (≥ 1) of convolution layer

phi-LSTM: A Phrase-based Hierarchical LSTM Model for Image ... · phi-LSTM: A Phrase-based Hierarchical LSTM Model for Image Captioning 3 the conventional RNN language model and our

A RNN-LSTM based Predictive Autoscaling Approach on ...ijsrcseit.com/Paper/CSEIT1833498.pdf3Department of Information Technology, PSG College of Technology, Coimbatore, Tamilnadu,

CSE 291G : Deep Learning for Sequences · RNN : LSTM • Overcome drawbacks of existing system • Account for variable length input and long term memory • Fails to handle cases

Stock Price Prediction Using RNN and LSTM

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6939740.pdf · Yelp review data, and report our generated sentences as being comparable to a traditional LSTM RNN

part 3 - GitHub Pages · Language models – predicting the next word using RNN/LSTM (Mikolov, 2012) Pixel RNN (van den Oord et al, ICML’16): predicting next pixel NADE (Larochelle

RNN/LSTM Data Assimilation for the Lorenz Chaotic Models · RNN/LSTM Data Assimilation for the Lorenz Chaotic Models Milt Halem, Harsh Vashistha University of Maryland, Baltimore

Large-Batch Training for LSTM and BeyondHowever, there are three problems in current large-batch research: (1) Although RNN techniques like LSTM [12] have been widely used in many

Multiple Instance Learning for Efﬁcient Sequential Data … · based on standard RNN models like LSTM are difﬁcult to deploy on tiny devices as they are computationally expensive

Advanced Prediction ModelsA Special RNN: LSTM • The gap between the relevant information and the point where it is needed can become unbounded • Empirical observation: Vanilla

A LSTM-RNN-Assisted Vector Tracking Loop for Signal Outage …downloads.hindawi.com/journals/ijae/2020/2975489.pdf · 2020. 8. 12. · Research Article A LSTM-RNN-Assisted Vector

Examensarbete Systemarkitekturutbildningen Philip ...hb.diva-portal.org/smash/get/diva2:1142444/FULLTEXT01.pdf · box trading, lstm, rnn, time series forecasting , prediction, tensorflow,

Why LSTM outperforms RNN - GitHub Pages...Solution - LSTM With the cell state, it runs straight down the entire chain, with only some minor linear interactions (NOT matrix multiplication

YoTube: Searching Action Proposal Via Recurrent and Static ... Searching... · A. Recurrent Neural Network Recurrent Neural Network (RNN), especially Long-Short Term Memory (LSTM)

CSE 291 - Advanced Statistical Natural Language Processing Nishant … · 2017-08-09 · Long Short-Term Memory (LSTM) I Hochreiter and Schmidhuber (1997) I An RNN architecture that

Enhancing Myanmar Speech Synthesis with Linguistic ...Myanmar TTS in [10]. In [11], more contextual information was applied in Myanmar speech synthesis. In this work, LSTM-RNN was