Deep Learning in 20 Minutes (or less!)

Size: px
Start display at page:

Download "Deep Learning in 20 Minutes (or less!)"

Transcription

1 1/19

2 2/19 Deep Learning in 20 Minutes (or less!) Group meeting May 23, 2016

3 3/19 Outline 1 Intro to Deep Learning 2 RNN 3 LSTM 4 Tools 5 Some useful links 6 Some useful (or maybe not) tips

4 4/19 Deep learning Figure : Artificial Neural Network (source: MLP (Multi Layer Perceptron) is just one type of deep learning Deep Neural Network is neural network with more layers

5 5/19 Deep learning Figure : Deep hierarchical representation of the input data learning feature hierarchies automatically learning features

6 6/19 Some good tutorials to start Machine Learning course by Prof. Andrew Ng (coursera). A tutorial on Deep Learning (Part 1) quocle/tutorial1.pdf How the Backpropagation works. An Introduction to Deep Learning. dwcorne/teaching/introdl.ppt

7 7/19 What are RNN The idea is to make use of sequential information. Some tasks: i.e. next word prediction, language model, POS tagging need information from the previous words. Recurrent: perform the same task for every element of sequence, with the output being depended on the previous computations. It has memory which captures information about what has been calculated so far. Can solve long dependency problems.

8 Function f is commonly a nonlinear function such as tanh or ReLU. 8/19 Recurrent Neural Network (RNN) Figure : RNN architecture (source: ) where x t is the input at time step t s t is the hidden state at time step t which acts as memory of the network o t is the output of at step t s t = f (Ux t + Ws t 1 + b) o t = softmax(vs t ) (1)

9 9/19 Recurrent Neural Network (RNN) Figure : Different RNN architecture types. From left to right: (1) Vanilla model(i.e. image classification). (2) Sequence output (i.e. image captioning takes an image and output a sentence of words). (3) Sequence input (i.e. sentiment analysis). (4) Sequence input and sequence output (i.e. Machine translation). (5) Synced sequence input and output (i.e. pos tagging) source:

10 Recurrent Neural Network (RNN) Figure : An example RNN with 4-dimensional input and output layers, and a hidden layer of 3 units (neurons). This diagram shows the activations in the forward pass when the RNN is fed the characters hell as input. The output layer contains confidences the RNN assigns for the next character (vocabulary is h,e,l,o ); source: 10/19

11 11/19 Backpropagation through Time (BPTT)(1) Figure : Training RNN using BPTT example E 3 δw = 3 k=0 E 3 δŷ 3 ŷ 3 δs 3 s 3 s k s k W (2)

12 12/19 Vanishing Gradient Problem Figure : Training RNN using BPTT problems E 3 δw = 3 k=0 E 3 δŷ 3 ŷ 3 δs 3 s 3 s k s k W Gradient contributions from far away steps become zero, and the state at those steps doesnt contribute to what you are learning: You end up not learning long-range dependencies. (3)

13 13/19 Long Short Term Memory (LSTM) Figure : Modules in LSTM LSTM has three gates: input gate i t : control what new information will be added to the cell state. forget gate f t : decide to what extent information from the previous state is forgotten output gate o t : filter the exposure of the internal memory cell of current state

14 14/19 Long Short Term Memory (LSTM) Figure : Modules in LSTM LSTM The gating mechanism is what allows LSTMs to explicitly model long-term dependencies. By learning the parameters for its gates, the network learns how its memory should behave. Each of the gate produces an output number between 0 and 1. A 1 represents completely keep this while a 0 represents completely get rid of this.

15 15/19 Deep Learning tools Comparison of deep learning software: of deep learning software Popular tools: 1 Theano ( python based; there are lots of Theano based DL framework including Keras ( and Lasagne ( 2 Torch ( implemented in LuaJIT 3 Caffe ( written in C++ and python.

16 16/19 Example Code-Keras print ( Build_model... ) model = Sequential () model. add ( Embedding ( max_features,128, input_length = maxlen, dropout =0.2)) model. add ( LSTM (128, dropout_w =0.2, dropout_u =0.2)) model. add ( Dense (1)) model. add ( Activation ( sigmoid ))

17 17/19 Tutorials, Courses and Blogs (CNN for Visual Recognition by Andrej Karpathy) (Deep Learning for NLP by Richard Socher) (Machine Learning course by Nando de Freitas) (by LISA Lab Univ of Montreal) (Deep Learning blog by Christopher Olah) (

18 18/19 Tips Start doing experiment! dive into the code To train a Neural Network: 1 Gradient check your implementation with a small batch of data 2 During training, monitor the loss, the training/validation accuracy. Implement early stopping if necessary. 3 Tuning the hyperparameters (i.e. learning rate, number of hidden layers, hidden layer size, updater algorithm) 4 Use regularization and dropout to prevent overfitting

19 19/19 Thank you!

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering

More information

Python Machine Learning

Python Machine Learning Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled

More information

Training a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski

Training a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski Training a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski Problem Statement and Background Given a collection of 8th grade science questions, possible answer

More information

Second Exam: Natural Language Parsing with Neural Networks

Second Exam: Natural Language Parsing with Neural Networks Second Exam: Natural Language Parsing with Neural Networks James Cross May 21, 2015 Abstract With the advent of deep learning, there has been a recent resurgence of interest in the use of artificial neural

More information

Глубокие рекуррентные нейронные сети для аспектно-ориентированного анализа тональности отзывов пользователей на различных языках

Глубокие рекуррентные нейронные сети для аспектно-ориентированного анализа тональности отзывов пользователей на различных языках Глубокие рекуррентные нейронные сети для аспектно-ориентированного анализа тональности отзывов пользователей на различных языках Тарасов Д. С. (dtarasov3@gmail.com) Интернет-портал reviewdot.ru, Казань,

More information

Deep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach

Deep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach #BaselOne7 Deep search Enhancing a search bar using machine learning Ilgün Ilgün & Cedric Reichenbach We are not researchers Outline I. Periscope: A search tool II. Goals III. Deep learning IV. Applying

More information

Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures

Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures Alex Graves and Jürgen Schmidhuber IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland TU Munich, Boltzmannstr.

More information

A Neural Network GUI Tested on Text-To-Phoneme Mapping

A Neural Network GUI Tested on Text-To-Phoneme Mapping A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis

More information

Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models

Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models Navdeep Jaitly 1, Vincent Vanhoucke 2, Geoffrey Hinton 1,2 1 University of Toronto 2 Google Inc. ndjaitly@cs.toronto.edu,

More information

HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION

HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION Atul Laxman Katole 1, Krishna Prasad Yellapragada 1, Amish Kumar Bedi 1, Sehaj Singh Kalra 1 and Mynepalli Siva Chaitanya 1 1 Samsung

More information

arxiv: v1 [cs.cv] 10 May 2017

arxiv: v1 [cs.cv] 10 May 2017 Inferring and Executing Programs for Visual Reasoning Justin Johnson 1 Bharath Hariharan 2 Laurens van der Maaten 2 Judy Hoffman 1 Li Fei-Fei 1 C. Lawrence Zitnick 2 Ross Girshick 2 1 Stanford University

More information

A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention

A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention Damien Teney 1, Peter Anderson 2*, David Golub 4*, Po-Sen Huang 3, Lei Zhang 3, Xiaodong He 3, Anton van den Hengel 1 1

More information

arxiv: v4 [cs.cl] 28 Mar 2016

arxiv: v4 [cs.cl] 28 Mar 2016 LSTM-BASED DEEP LEARNING MODELS FOR NON- FACTOID ANSWER SELECTION Ming Tan, Cicero dos Santos, Bing Xiang & Bowen Zhou IBM Watson Core Technologies Yorktown Heights, NY, USA {mingtan,cicerons,bingxia,zhou}@us.ibm.com

More information

Model Ensemble for Click Prediction in Bing Search Ads

Model Ensemble for Click Prediction in Bing Search Ads Model Ensemble for Click Prediction in Bing Search Ads Xiaoliang Ling Microsoft Bing xiaoling@microsoft.com Hucheng Zhou Microsoft Research huzho@microsoft.com Weiwei Deng Microsoft Bing dedeng@microsoft.com

More information

(Sub)Gradient Descent

(Sub)Gradient Descent (Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include

More information

arxiv: v1 [cs.lg] 7 Apr 2015

arxiv: v1 [cs.lg] 7 Apr 2015 Transferring Knowledge from a RNN to a DNN William Chan 1, Nan Rosemary Ke 1, Ian Lane 1,2 Carnegie Mellon University 1 Electrical and Computer Engineering, 2 Language Technologies Institute Equal contribution

More information

POS tagging of Chinese Buddhist texts using Recurrent Neural Networks

POS tagging of Chinese Buddhist texts using Recurrent Neural Networks POS tagging of Chinese Buddhist texts using Recurrent Neural Networks Longlu Qin Department of East Asian Languages and Cultures longlu@stanford.edu Abstract Chinese POS tagging, as one of the most important

More information

Artificial Neural Networks written examination

Artificial Neural Networks written examination 1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14

More information

Dropout improves Recurrent Neural Networks for Handwriting Recognition

Dropout improves Recurrent Neural Networks for Handwriting Recognition 2014 14th International Conference on Frontiers in Handwriting Recognition Dropout improves Recurrent Neural Networks for Handwriting Recognition Vu Pham,Théodore Bluche, Christopher Kermorvant, and Jérôme

More information

ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF

ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Read Online and Download Ebook ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Click link bellow and free register to download

More information

arxiv: v1 [cs.lg] 15 Jun 2015

arxiv: v1 [cs.lg] 15 Jun 2015 Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy arxiv:1506.04477v1 [cs.lg] 15 Jun 2015 Sang-Woo Lee Min-Oh Heo School of Computer Science and

More information

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation

A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP-2016 October 11-12 Natalia Tomashenko 1,2,3 natalia.tomashenko@univ-lemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick

More information

Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks

Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks Devendra Singh Chaplot, Eunhee Rhim, and Jihie Kim Samsung Electronics Co., Ltd. Seoul, South Korea {dev.chaplot,eunhee.rhim,jihie.kim}@samsung.com

More information

Semantic Segmentation with Histological Image Data: Cancer Cell vs. Stroma

Semantic Segmentation with Histological Image Data: Cancer Cell vs. Stroma Semantic Segmentation with Histological Image Data: Cancer Cell vs. Stroma Adam Abdulhamid Stanford University 450 Serra Mall, Stanford, CA 94305 adama94@cs.stanford.edu Abstract With the introduction

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should

More information

Cultivating DNN Diversity for Large Scale Video Labelling

Cultivating DNN Diversity for Large Scale Video Labelling Cultivating DNN Diversity for Large Scale Video Labelling Mikel Bober-Irizar mikel@mxbi.net Sameed Husain sameed.husain@surrey.ac.uk Miroslaw Bober m.bober@surrey.ac.uk Eng-Jon Ong e.ong@surrey.ac.uk Abstract

More information

UNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS. Heiga Zen, Haşim Sak

UNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS. Heiga Zen, Haşim Sak UNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS Heiga Zen, Haşim Sak Google fheigazen,hasimg@google.com ABSTRACT Long short-term

More information

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing Ask Me Anything: Dynamic Memory Networks for Natural Language Processing Ankit Kumar*, Ozan Irsoy*, Peter Ondruska*, Mohit Iyyer*, James Bradbury, Ishaan Gulrajani*, Victor Zhong*, Romain Paulus, Richard

More information

arxiv: v1 [cs.cl] 27 Apr 2016

arxiv: v1 [cs.cl] 27 Apr 2016 The IBM 2016 English Conversational Telephone Speech Recognition System George Saon, Tom Sercu, Steven Rennie and Hong-Kwang J. Kuo IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598 gsaon@us.ibm.com

More information

Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE

Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE EE-589 Introduction to Neural Assistant Prof. Dr. Turgay IBRIKCI Room # 305 (322) 338 6868 / 139 Wensdays 9:00-12:00 Course Outline The course is divided in two parts: theory and practice. 1. Theory covers

More information

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Stephan Gouws and GJ van Rooyen MIH Medialab, Stellenbosch University SOUTH AFRICA {stephan,gvrooyen}@ml.sun.ac.za

More information

INPE São José dos Campos

INPE São José dos Campos INPE-5479 PRE/1778 MONLINEAR ASPECTS OF DATA INTEGRATION FOR LAND COVER CLASSIFICATION IN A NEDRAL NETWORK ENVIRONNENT Maria Suelena S. Barros Valter Rodrigues INPE São José dos Campos 1993 SECRETARIA

More information

TRANSFER LEARNING OF WEAKLY LABELLED AUDIO. Aleksandr Diment, Tuomas Virtanen

TRANSFER LEARNING OF WEAKLY LABELLED AUDIO. Aleksandr Diment, Tuomas Virtanen TRANSFER LEARNING OF WEAKLY LABELLED AUDIO Aleksandr Diment, Tuomas Virtanen Tampere University of Technology Laboratory of Signal Processing Korkeakoulunkatu 1, 33720, Tampere, Finland firstname.lastname@tut.fi

More information

Attributed Social Network Embedding

Attributed Social Network Embedding JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, MAY 2017 1 Attributed Social Network Embedding arxiv:1705.04969v1 [cs.si] 14 May 2017 Lizi Liao, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua Abstract Embedding

More information

Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model

Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Xinying Song, Xiaodong He, Jianfeng Gao, Li Deng Microsoft Research, One Microsoft Way, Redmond, WA 98052, U.S.A.

More information

arxiv: v1 [cs.cl] 20 Jul 2015

arxiv: v1 [cs.cl] 20 Jul 2015 How to Generate a Good Word Embedding? Siwei Lai, Kang Liu, Liheng Xu, Jun Zhao National Laboratory of Pattern Recognition (NLPR) Institute of Automation, Chinese Academy of Sciences, China {swlai, kliu,

More information

Test Effort Estimation Using Neural Network

Test Effort Estimation Using Neural Network J. Software Engineering & Applications, 2010, 3: 331-340 doi:10.4236/jsea.2010.34038 Published Online April 2010 (http://www.scirp.org/journal/jsea) 331 Chintala Abhishek*, Veginati Pavan Kumar, Harish

More information

CS 446: Machine Learning

CS 446: Machine Learning CS 446: Machine Learning Introduction to LBJava: a Learning Based Programming Language Writing classifiers Christos Christodoulopoulos Parisa Kordjamshidi Motivation 2 Motivation You still have not learnt

More information

Deep Neural Network Language Models

Deep Neural Network Language Models Deep Neural Network Language Models Ebru Arısoy, Tara N. Sainath, Brian Kingsbury, Bhuvana Ramabhadran IBM T.J. Watson Research Center Yorktown Heights, NY, 10598, USA {earisoy, tsainath, bedk, bhuvana}@us.ibm.com

More information

THE world surrounding us involves multiple modalities

THE world surrounding us involves multiple modalities 1 Multimodal Machine Learning: A Survey and Taxonomy Tadas Baltrušaitis, Chaitanya Ahuja, and Louis-Philippe Morency arxiv:1705.09406v2 [cs.lg] 1 Aug 2017 Abstract Our experience of the world is multimodal

More information

Human Emotion Recognition From Speech

Human Emotion Recognition From Speech RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati

More information

Axiom 2013 Team Description Paper

Axiom 2013 Team Description Paper Axiom 2013 Team Description Paper Mohammad Ghazanfari, S Omid Shirkhorshidi, Farbod Samsamipour, Hossein Rahmatizadeh Zagheli, Mohammad Mahdavi, Payam Mohajeri, S Abbas Alamolhoda Robotics Scientific Association

More information

I-COMPETERE: Using Applied Intelligence in search of competency gaps in software project managers.

I-COMPETERE: Using Applied Intelligence in search of competency gaps in software project managers. Information Systems Frontiers manuscript No. (will be inserted by the editor) I-COMPETERE: Using Applied Intelligence in search of competency gaps in software project managers. Ricardo Colomo-Palacios

More information

A deep architecture for non-projective dependency parsing

A deep architecture for non-projective dependency parsing Universidade de São Paulo Biblioteca Digital da Produção Intelectual - BDPI Departamento de Ciências de Computação - ICMC/SCC Comunicações em Eventos - ICMC/SCC 2015-06 A deep architecture for non-projective

More information

arxiv: v5 [cs.ai] 18 Aug 2015

arxiv: v5 [cs.ai] 18 Aug 2015 When Are Tree Structures Necessary for Deep Learning of Representations? Jiwei Li 1, Minh-Thang Luong 1, Dan Jurafsky 1 and Eduard Hovy 2 1 Computer Science Department, Stanford University, Stanford, CA

More information

ON THE USE OF WORD EMBEDDINGS ALONE TO

ON THE USE OF WORD EMBEDDINGS ALONE TO ON THE USE OF WORD EMBEDDINGS ALONE TO REPRESENT NATURAL LANGUAGE SEQUENCES Anonymous authors Paper under double-blind review ABSTRACT To construct representations for natural language sequences, information

More information

Adaptive learning based on cognitive load using artificial intelligence and electroencephalography

Adaptive learning based on cognitive load using artificial intelligence and electroencephalography Adaptive learning based on cognitive load using artificial intelligence and electroencephalography Håkon Jarle Hassel Master of Science in Computer Science Submission date: June 2017 Supervisor: Asbjørn

More information

Twitter Sentiment Classification on Sanders Data using Hybrid Approach

Twitter Sentiment Classification on Sanders Data using Hybrid Approach IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 118-123 www.iosrjournals.org Twitter Sentiment Classification on Sanders

More information

Evolutive Neural Net Fuzzy Filtering: Basic Description

Evolutive Neural Net Fuzzy Filtering: Basic Description Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:

More information

Artificial Neural Networks

Artificial Neural Networks Artificial Neural Networks Andres Chavez Math 382/L T/Th 2:00-3:40 April 13, 2010 Chavez2 Abstract The main interest of this paper is Artificial Neural Networks (ANNs). A brief history of the development

More information

Assignment 1: Predicting Amazon Review Ratings

Assignment 1: Predicting Amazon Review Ratings Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for

More information

Semi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17.

Semi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17. Semi-supervised methods of text processing, and an application to medical concept extraction Yacine Jernite Text-as-Data series September 17. 2015 What do we want from text? 1. Extract information 2. Link

More information

CS224d Deep Learning for Natural Language Processing. Richard Socher, PhD

CS224d Deep Learning for Natural Language Processing. Richard Socher, PhD CS224d Deep Learning for Natural Language Processing, PhD Welcome 1. CS224d logis7cs 2. Introduc7on to NLP, deep learning and their intersec7on 2 Course Logis>cs Instructor: (Stanford PhD, 2014; now Founder/CEO

More information

arxiv: v3 [cs.cl] 7 Feb 2017

arxiv: v3 [cs.cl] 7 Feb 2017 NEWSQA: A MACHINE COMPREHENSION DATASET Adam Trischler Tong Wang Xingdi Yuan Justin Harris Alessandro Sordoni Philip Bachman Kaheer Suleman {adam.trischler, tong.wang, eric.yuan, justin.harris, alessandro.sordoni,

More information

A study of speaker adaptation for DNN-based speech synthesis

A study of speaker adaptation for DNN-based speech synthesis A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu, Pawel Swietojanski, Christophe Veaux, Steve Renals, Simon King The Centre for Speech Technology Research (CSTR) University of Edinburgh,

More information

Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture

Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture Yuanliang Meng, Anna Rumshisky, Alexey Romanov {ymeng,arum,aromanov}@cs.uml.edu Department

More information

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina

More information

A Deep Bag-of-Features Model for Music Auto-Tagging

A Deep Bag-of-Features Model for Music Auto-Tagging 1 A Deep Bag-of-Features Model for Music Auto-Tagging Juhan Nam, Member, IEEE, Jorge Herrera, and Kyogu Lee, Senior Member, IEEE latter is often referred to as music annotation and retrieval, or simply

More information

There are some definitions for what Word

There are some definitions for what Word Word Embeddings and Their Use In Sentence Classification Tasks Amit Mandelbaum Hebrew University of Jerusalm amit.mandelbaum@mail.huji.ac.il Adi Shalev bitan.adi@gmail.com arxiv:1610.08229v1 [cs.lg] 26

More information

Modeling function word errors in DNN-HMM based LVCSR systems

Modeling function word errors in DNN-HMM based LVCSR systems Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford

More information

CSL465/603 - Machine Learning

CSL465/603 - Machine Learning CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am

More information

Softprop: Softmax Neural Network Backpropagation Learning

Softprop: Softmax Neural Network Backpropagation Learning Softprop: Softmax Neural Networ Bacpropagation Learning Michael Rimer Computer Science Department Brigham Young University Provo, UT 84602, USA E-mail: mrimer@axon.cs.byu.edu Tony Martinez Computer Science

More information

Residual Stacking of RNNs for Neural Machine Translation

Residual Stacking of RNNs for Neural Machine Translation Residual Stacking of RNNs for Neural Machine Translation Raphael Shu The University of Tokyo shu@nlab.ci.i.u-tokyo.ac.jp Akiva Miura Nara Institute of Science and Technology miura.akiba.lr9@is.naist.jp

More information

Syntactic systematicity in sentence processing with a recurrent self-organizing network

Syntactic systematicity in sentence processing with a recurrent self-organizing network Syntactic systematicity in sentence processing with a recurrent self-organizing network Igor Farkaš,1 Department of Applied Informatics, Comenius University Mlynská dolina, 842 48 Bratislava, Slovak Republic

More information

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems Ajith Abraham School of Business Systems, Monash University, Clayton, Victoria 3800, Australia. Email: ajith.abraham@ieee.org

More information

Issues in the Mining of Heart Failure Datasets

Issues in the Mining of Heart Failure Datasets International Journal of Automation and Computing 11(2), April 2014, 162-179 DOI: 10.1007/s11633-014-0778-5 Issues in the Mining of Heart Failure Datasets Nongnuch Poolsawad 1 Lisa Moore 1 Chandrasekhar

More information

A JOINT MANY-TASK MODEL: GROWING A NEURAL NETWORK FOR MULTIPLE NLP TASKS

A JOINT MANY-TASK MODEL: GROWING A NEURAL NETWORK FOR MULTIPLE NLP TASKS A JOINT MANY-TASK MODEL: GROWING A NEURAL NETWORK FOR MULTIPLE NLP TASKS Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka & Richard Socher The University of Tokyo {hassy, tsuruoka}@logos.t.u-tokyo.ac.jp

More information

Knowledge Transfer in Deep Convolutional Neural Nets

Knowledge Transfer in Deep Convolutional Neural Nets Knowledge Transfer in Deep Convolutional Neural Nets Steven Gutstein, Olac Fuentes and Eric Freudenthal Computer Science Department University of Texas at El Paso El Paso, Texas, 79968, U.S.A. Abstract

More information

arxiv: v1 [cs.lg] 20 Mar 2017

arxiv: v1 [cs.lg] 20 Mar 2017 Dance Dance Convolution Chris Donahue 1, Zachary C. Lipton 2, and Julian McAuley 2 1 Department of Music, University of California, San Diego 2 Department of Computer Science, University of California,

More information

SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF)

SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) Hans Christian 1 ; Mikhael Pramodana Agus 2 ; Derwin Suhartono 3 1,2,3 Computer Science Department,

More information

Speaker Identification by Comparison of Smart Methods. Abstract

Speaker Identification by Comparison of Smart Methods. Abstract Journal of mathematics and computer science 10 (2014), 61-71 Speaker Identification by Comparison of Smart Methods Ali Mahdavi Meimand Amin Asadi Majid Mohamadi Department of Electrical Department of Computer

More information

arxiv: v2 [cs.ir] 22 Aug 2016

arxiv: v2 [cs.ir] 22 Aug 2016 Exploring Deep Space: Learning Personalized Ranking in a Semantic Space arxiv:1608.00276v2 [cs.ir] 22 Aug 2016 ABSTRACT Jeroen B. P. Vuurens The Hague University of Applied Science Delft University of

More information

SARDNET: A Self-Organizing Feature Map for Sequences

SARDNET: A Self-Organizing Feature Map for Sequences SARDNET: A Self-Organizing Feature Map for Sequences Daniel L. James and Risto Miikkulainen Department of Computer Sciences The University of Texas at Austin Austin, TX 78712 dljames,risto~cs.utexas.edu

More information

Machine Learning from Garden Path Sentences: The Application of Computational Linguistics

Machine Learning from Garden Path Sentences: The Application of Computational Linguistics Machine Learning from Garden Path Sentences: The Application of Computational Linguistics http://dx.doi.org/10.3991/ijet.v9i6.4109 J.L. Du 1, P.F. Yu 1 and M.L. Li 2 1 Guangdong University of Foreign Studies,

More information

On the Formation of Phoneme Categories in DNN Acoustic Models

On the Formation of Phoneme Categories in DNN Acoustic Models On the Formation of Phoneme Categories in DNN Acoustic Models Tasha Nagamine Department of Electrical Engineering, Columbia University T. Nagamine Motivation Large performance gap between humans and state-

More information

THE enormous growth of unstructured data, including

THE enormous growth of unstructured data, including INTL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS, 2014, VOL. 60, NO. 4, PP. 321 326 Manuscript received September 1, 2014; revised December 2014. DOI: 10.2478/eletel-2014-0042 Deep Image Features in

More information

Computerized Adaptive Psychological Testing A Personalisation Perspective

Computerized Adaptive Psychological Testing A Personalisation Perspective Psychology and the internet: An European Perspective Computerized Adaptive Psychological Testing A Personalisation Perspective Mykola Pechenizkiy mpechen@cc.jyu.fi Introduction Mixed Model of IRT and ES

More information

Word Segmentation of Off-line Handwritten Documents

Word Segmentation of Off-line Handwritten Documents Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department

More information

Dialog-based Language Learning

Dialog-based Language Learning Dialog-based Language Learning Jason Weston Facebook AI Research, New York. jase@fb.com arxiv:1604.06045v4 [cs.cl] 20 May 2016 Abstract A long-term goal of machine learning research is to build an intelligent

More information

Learning Methods for Fuzzy Systems

Learning Methods for Fuzzy Systems Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8

More information

Georgetown University at TREC 2017 Dynamic Domain Track

Georgetown University at TREC 2017 Dynamic Domain Track Georgetown University at TREC 2017 Dynamic Domain Track Zhiwen Tang Georgetown University zt79@georgetown.edu Grace Hui Yang Georgetown University huiyang@cs.georgetown.edu Abstract TREC Dynamic Domain

More information

Modeling function word errors in DNN-HMM based LVCSR systems

Modeling function word errors in DNN-HMM based LVCSR systems Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford

More information

*** * * * COUNCIL * * CONSEIL OFEUROPE * * * DE L'EUROPE. Proceedings of the 9th Symposium on Legal Data Processing in Europe

*** * * * COUNCIL * * CONSEIL OFEUROPE * * * DE L'EUROPE. Proceedings of the 9th Symposium on Legal Data Processing in Europe *** * * * COUNCIL * * CONSEIL OFEUROPE * * * DE L'EUROPE Proceedings of the 9th Symposium on Legal Data Processing in Europe Bonn, 10-12 October 1989 Systems based on artificial intelligence in the legal

More information

Lecture 10: Reinforcement Learning

Lecture 10: Reinforcement Learning Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation

More information

CS Machine Learning

CS Machine Learning CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing

More information

CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH

CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH ISSN: 0976-3104 Danti and Bhushan. ARTICLE OPEN ACCESS CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH Ajit Danti 1 and SN Bharath Bhushan 2* 1 Department

More information

Using the Artificial Neural Networks for Identification Unknown Person

Using the Artificial Neural Networks for Identification Unknown Person IOSR Journal of Dental and Medical Sciences (IOSR-JDMS) e-issn: 2279-0853, p-issn: 2279-0861.Volume 16, Issue 4 Ver. III (April. 2017), PP 107-113 www.iosrjournals.org Using the Artificial Neural Networks

More information

Analysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription

Analysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription Analysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription Wilny Wilson.P M.Tech Computer Science Student Thejus Engineering College Thrissur, India. Sindhu.S Computer

More information

Time series prediction

Time series prediction Chapter 13 Time series prediction Amaury Lendasse, Timo Honkela, Federico Pouzols, Antti Sorjamaa, Yoan Miche, Qi Yu, Eric Severin, Mark van Heeswijk, Erkki Oja, Francesco Corona, Elia Liitiäinen, Zhanxing

More information

Distributed Learning of Multilingual DNN Feature Extractors using GPUs

Distributed Learning of Multilingual DNN Feature Extractors using GPUs Distributed Learning of Multilingual DNN Feature Extractors using GPUs Yajie Miao, Hao Zhang, Florian Metze Language Technologies Institute, School of Computer Science, Carnegie Mellon University Pittsburgh,

More information

Memory-based grammatical error correction

Memory-based grammatical error correction Memory-based grammatical error correction Antal van den Bosch Peter Berck Radboud University Nijmegen Tilburg University P.O. Box 9103 P.O. Box 90153 NL-6500 HD Nijmegen, The Netherlands NL-5000 LE Tilburg,

More information

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,

More information

Disciplinary Literacy in Science

Disciplinary Literacy in Science Disciplinary Literacy in Science 18 th UCF Literacy Symposium 4/1/2016 Vicky Zygouris-Coe, Ph.D. UCF, CEDHP vzygouri@ucf.edu April 1, 2016 Objectives Examine the benefits of disciplinary literacy for science

More information

A Review: Speech Recognition with Deep Learning Methods

A Review: Speech Recognition with Deep Learning Methods Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 5, May 2015, pg.1017

More information

An investigation of imitation learning algorithms for structured prediction

An investigation of imitation learning algorithms for structured prediction JMLR: Workshop and Conference Proceedings 24:143 153, 2012 10th European Workshop on Reinforcement Learning An investigation of imitation learning algorithms for structured prediction Andreas Vlachos Computer

More information

Intervening to alleviate word-finding difficulties in children: case series data and a computational modelling foundation

Intervening to alleviate word-finding difficulties in children: case series data and a computational modelling foundation PCGN1003204 Techset Composition India (P) Ltd., Bangalore and Chennai, India 1/20/2015 Cognitive Neuropsychology, 2015 http://dx.doi.org/10.1080/02643294.2014.1003204 5 Intervening to alleviate word-finding

More information

Indian Institute of Technology, Kanpur

Indian Institute of Technology, Kanpur Indian Institute of Technology, Kanpur Course Project - CS671A POS Tagging of Code Mixed Text Ayushman Sisodiya (12188) {ayushmn@iitk.ac.in} Donthu Vamsi Krishna (15111016) {vamsi@iitk.ac.in} Sandeep Kumar

More information

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1 Notes on The Sciences of the Artificial Adapted from a shorter document written for course 17-652 (Deciding What to Design) 1 Ali Almossawi December 29, 2005 1 Introduction The Sciences of the Artificial

More information