Lecture 10 Summary and reflections
|
|
- Peter Todd
- 5 years ago
- Views:
Transcription
1 Lecture 10 Summary and reflections Niklas Wahlström Division of Systems and Control Department of Information Technology Uppsala University. SML - Lecture 10
2 Contents Lecture Summary of Lecture 9 2. Summary of the laboratory work 3. Summary of the whole course 4. Outlook: a few words about things that we have not covered 5. New course! 1 / 26 SML - Lecture 10
3 Summary of Lecture 9 (I/IV) Convolutional layer Consider a hidden layer with 6 6 hidden units. Dense layer: Each hidden unit is connected with all pixels. Each pixel-hidden-unit-pair has its own unique parameter. 2 / 26 SML - Lecture 10 Input variables x 1,1 x 1,2 x 1,3 x 1,4 x 1,5 x 1,6 x 2,1 x 2,2 x 2,3 x 2,4 x 2,5 x 2,6 x 3,1 x 3,2 x 3,3 x 3,4 x 3,5 x 3,6 x 4,1 x 4,2 x 4,3 x 4,4 x 4,5 x 4,6 x 5,1 x 5,2 x 5,3 x 5,4 x 5,5 x 5,6 x 6,1 x 6,2 x 6,3 x 6,4 x 6,5 x 6,6 1 Hidden units σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ
4 Summary of Lecture 9 (I/IV) Convolutional layer Consider a hidden layer with 6 6 hidden units. Dense layer: Each hidden unit is connected with all pixels. Each pixel-hidden-unit-pair has its own unique parameter. Convolutional layer: Each hidden unit is connected with a region of pixels via a set of parameters, so-called kernel. Different hidden units have the same set of parameters. 2 / 26 SML - Lecture 10 Input variables x 1,1 x 1,2 x 1,3 x 1,4 x 1,5 x 1,6 x 2,1 x 2,2 x 2,3 x 2,4 x 2,5 x 2,6 x 3,1 x 3,2 x 3,3 x 3,4 x 3,5 x 3,6 x 4,1 x 4,2 x 4,3 x 4,4 x 4,5 x 4,6 x 5,1 x 5,2 x 5,3 x 5,4 x 5,5 x 5,6 x 6,1 x 6,2 x 6,3 x 6,4 x 6,5 x 6,6 1 β (1) 1,3 β (1) 3,3 β (1) 0 Hidden units σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ σ
5 Summary of Lecture 9 (I/IV) Convolutional layer Consider a hidden layer with 6 6 hidden units. Dense layer: Each hidden unit is connected with all pixels. Each pixel-hidden-unit-pair has its own unique parameter. Convolutional layer: Each hidden unit is connected with a region of pixels via a set of parameters, so-called kernel. Different hidden units have the same set of parameters. Input variables x 1,1 x 1,2 x 1,3 x 1,4 x 1,5 x 1,6 1 β (1) 0 Hidden units σ σ σ σ σ σ x 2,1 x 2,2 x 2,3 x 2,4 x 2,5 x 2,6 x 3,1 x 3,2 x 3,3 x 3,4 x 3,5 x 3,6 β (1) 1,3 σ σ σ σ σ σ σ σ σ σ σ σ x 4,1 x 4,2 x 4,3 x 4,4 x 4,5 x 4,6 x 5,1 x 5,2 x 5,3 x 5,4 x 5,5 x 5,6 β (1) 3,3 σ σ σ σ σ σ σ σ σ σ σ σ x 6,1 x 6,2 x 6,3 x 6,4 x 6,5 x 6,6 σ σ σ σ σ σ 2 / 26 SML - Lecture 10
6 Summary of Lecture 9 (II/IV) Convolutional neural network (CNN) A full CNN usually consist of multiple convolutional layers (here three),......and a few final dense layers (here two). Input variables Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 size Hidden Hidden Hidden Hidden Output Type: Convolutional Type: Convolutional Type: Convolutional Type: Dense Type: Dense Size: Size: Size: Size: 200 Size: 10 Kernel rows and columns: (5 5) Kernel rows and columns: (5 5) Kernel row and columns: (4 4) Stride: [1, 1] Stride: [2, 2] Stride: [2, 2] Predicted class probabilities Size: 10 p(y = 1 x; θ) p(y = 2 x; θ) p(y = 3 x; θ) p(y = 4 x; θ) p(y = 5 x; θ) p(y = 6 x; θ). p(y = 7 x; θ) p(y = 8 x; θ) p(y = 9 x; θ) p(y = 10 x; θ) 3 / 26 SML - Lecture 10
7 Summary of Lecture 9 (III/IV) Training a neural network We train a network by considering the optimization problem θ = arg min θ J(θ), J(θ) = 1 n θ all parameters of the network θ the estimated parameters n L(x i, y i, θ) i=1 {x i, y i } n i=1 the training data L(x i, y i, θ) the loss function (for example cross-entropy) J(θ) the cost function 4 / 26 SML - Lecture 10
8 Summary of Lecture 9 (IV/IV) Stochastic gradient descent At each optimization step we need to compute the gradient g t = θ J(θ t ) = 1 n θ L(x i, y n i, θ t ). i=1 Challenge - n is big - expensive to compute gradient. Solution: For each iteration, we only use a small random batch of the data set a mini-batch to compute the gradient g t. This procedure is called the stochastic gradient descent. { Training data (reshuffled) }} { x 19 x 16 x 18 x 6 x 9 x 13 x 1 x 14 x 20 x 11 x 3 x 8 x 7 x 12 x 4 x 17 x 5 x 10 x 2 x 15 y 19 y 16 y 18 y 6 y 9 y 13 y 1 y 14 y 20 y 11 y 3 y 8 y 7 y 12 y 4 y 17 y 5 y 10 y 2 y 15 Iteration: 6 Epoch: 2 5 / 26 SML - Lecture 10
9 Summary of laboratory work One layer neural network (logistic regression) Trained for iterations. SGD with learning rate: γ = / 26 SML - Lecture 10
10 Summary of laboratory work Two layer neural network with sigmoid activation function. Trained for iterations. SGD with learning rate: γ = 0.5 Significantly better performance. 6 / 26 SML - Lecture 10
11 Summary of laboratory work Five layer neural network with sigmoid activation function. Trained for iterations. SGD with learning rate: γ = 0.5 Convergence slow, not yet converged. 6 / 26 SML - Lecture 10
12 Summary of laboratory work Five layer neural network with ReLU activation function. Trained for iterations. SGD with learning rate: γ = 0.5 It trains much faster! 6 / 26 SML - Lecture 10
13 Summary of laboratory work Five layer neural network with ReLU activation function. Trained for iterations. Adam with learning rate: γ = / 26 SML - Lecture 10 Not a bigg difference with Adam optimizer (but it is important in the CNN part!)
14 Summary of laboratory work CNN - three conv layers, two dense layers Channels/units: , Kernels 5x5str1-5x5str2-4x4str2 Adam with learning rate: γ = / 26 SML - Lecture 10 CNN increases performance! Cost function oscillates decrease learning rate
15 Summary of laboratory work Extras! CNN - three conv layers, two dense layers Channels/units: , Kernels 5x5str1-5x5str2-4x4str2 Adam with decaying learning rate from: γ = to γ = And now we start to overfit... Regularize! 7 / 26 SML - Lecture 10
16 Summary of laboratory work Extras! CNN - three conv layers, two dense layers Channels/units: , Kernels 5x5str1-5x5str2-4x4str2 Adam with decaying learning rate from: γ = to γ = Dropout with p = 0.75 on units in last hidden layer. Better cross-entropy, and now also an improvement in accuracy!! 7 / 26 SML - Lecture 10
17 Summary of laboratory work Extras! CNN - three conv layers, two dense layers Channels/units: , Kernels 6x6str1-5x5str2-4x4str2 Adam with decaying learning rate from: γ = to γ = Dropout with p = 0.75 on units in last hidden layer. This was the best I could get. Did you get any better? 7 / 26 SML - Lecture 10
18 This course Machine learning gives computers the ability to solve problems without being explicitly programmed for the task at hand. This is done by learning from examples, i.e. from training data. Data on its own is typically useless, it is only when we can extract knowledge from the data that it becomes useful. Specifically, we have studied supervised learning methods, in which we build a model of the relationship between an input variable x and an output variable y. 8 / 26 SML - Lecture 10
19 Supervised Machine Learning Learning a model from labeled data. Training data Labels e.g. mat, mirror, boat,... Learning algorithm Model 9 / 26 SML - Lecture 10
20 Supervised Machine Learning Using the learned model on new previously unseen data. Unseen data? Model prediction The model must generalize to new unseen data. example images from two disease classes. These test images highligh difficulty of malignant versus benign discernment for the three med 10 / 26 SML - Lecture 10
21 Inputs and outputs The input x is composed of all the available variables which are believed to be relevant for predicting the value of the output y. We have considered the case where we have p input variables, x = (x j ) p j=1, and one output variable y. Both the inputs x j and the output y can be either quantitative (can be ordered), or qualitative (takes values in an unordered set). 11 / 26 SML - Lecture 10
22 Regression and classification Regression Classification Output, y quantitative qualitative Inputs, x j Model ( conceptual ) quantitative or qualitative y = f(x) + ε quantitative or qualitative p(y = k x), k = 1,..., K 12 / 26 SML - Lecture 10
23 Bias-variance E new : How well a method will perform for unseen data. Bias: The inability of a model to describe the training data. Variance: How sensitive a model is to the training data. E new = bias 2 + variance + irreducible error 13 / 26 SML - Lecture 10
24 Bias-variance Underfit Overfit Ē new Irreducible error Error Variance Bias 2 14 / 26 SML - Lecture 10 Model complexity
25 Cross validation To estimate E new, we can use cross-validation. 1st iteration 2nd iteration Training data Validation data Validation data cth iteration Validation data. Training data When using cross validation to select, e.g., inputs and hyperparameters, there is a risk of overfitting! (But it can still be the best available option... ) 15 / 26 SML - Lecture 10
26 Regularization Regularization offers a way to decrease the model complexity (and hence risk of overfitting) Ridge Regression: add a penalty term λ β 2 2 LASSO: add a penalty term λ β 1 can result in sparse solutions Select λ, e.g. by cross validation! There are also other ways to change the model complexity: Increase k in k-nn Bagging 16 / 26 SML - Lecture 10
27 Parametric vs. nonparametric models Parametric models Parameterized by a finite-dimensional parameter θ Training/learning the model = estimating θ Once θ is estimated, the predictions depend only on θ (not the training data) ex) Linear regression, LDA, QDA, Neural Networks Nonparametric models The model flexibility is allowed to grow with the amount of available data Predictions depend directly on the training data Can be viewed as having an infinite number of parameters ex) k-nn, CART 17 / 26 SML - Lecture 10
28 Ensemble methods Ensemble methods are a type of meta algorithms : Construct one powerful model from multiple base models (=ensemble members), each of which may perform poorly on its own! We have encountered two such approaches: 1. Bagging: Reduce variance of low-bias/high-variance models by bootstrap aggregation 2. Boosting: Construct weak base models sequentially, so that each model tries to correct the mistakes of the previous one 18 / 26 SML - Lecture 10
29 A toolbox of methods Regression Classification Non-parametric Parametric Ensemble Linear regression Logistic regression LDA QDA k-nn CART Random Forests AdaBoost ( ) (Deep) Neural nets 19 / 26 SML - Lecture 10
30 Summary for the exam (in one slide) Classification and regression problem formulations Parametric and non-parametric models Inputs and outputs / quantitative and qualitative variables Decision boundaries / linear vs. nonlinear classifiers Cross-validation (the purpose!) and model testing Bias-variance trade-off / model flexibility / over-fitting Regularization / ridge regression and LASSO The different methods discussed throughout the course 20 / 26 SML - Lecture 10
31 Summary for life What should you remember from statistical machine learning? The problem formulations: regression and classification The existence of different types of methods The bias-variance trade-off and cross validation The possibilities: Machine learning can be used for an extremely wide range of applications and data types The TSTF principle: Try simple things first! 21 / 26 SML - Lecture 10
32 Outlook: Unsupervised learning Regression and classification are supervised learning problems The models are trained using both inputs x and outputs y. Unsupervised learning methods tries to find patterns in unlabeled data, i.e. we train the models from just the x. Dimensionality reduction / manifold learning Cluster analysis Generative model learning Blind source separation 22 / 26 SML - Lecture 10
33 Outlook: Reinforcement learning A reinforcement learning system is asked to take actions that influence its environment in order to maximize a reward. Contrary to supervised learning, the correct input/output pair is not revealed learning has to be carried out based on the reward feedback often a focus on online performance ( exploration-exploitation trade-off) 23 / 26 SML - Lecture 10
34 New course!! Advanced probabilistic machine learning Contents (very brief): Probabilistic/Bayesian modeling Bayesian linear regression Graphical models Gaussian processes Variational inference Monte Carlo methods Unsupervised learning Variational autoencoders Examination: Mini-project, lab, oral exam. When: Period 1, running every year starting this fall. Info: 24 / 26 SML - Lecture 10
35 overlap is needed to solve this problem. Our Machine Learning research ultrabrief Monte Carlo methods (especially sequential Monte Carlo) Deep learning Gaussian processes The use of probabilistic programming he object detection marking an obstacle that con- driving (with Autoliv), digital Applications: Autonomous rts. In this case a curb, a speed bump and a traffic pathology (withroad Sectra), input and to the right the estimated surface etc. ne (white) and the detected obstacle (purple). We take a particular interest in nonlinear dynamical systems. wo25objects receiving / 26 SML - Lecturethe 10 same label when coming
36 Thank you! Machine learning gives computers the ability to solve problems without being explicitly programmed for the task at hand. Thank you for your attention and good luck in the future!!! 26 / 26 SML - Lecture 10
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 informationPython 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 informationGenerative models and adversarial training
Day 4 Lecture 1 Generative models and adversarial training Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University What is a generative model?
More information(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 informationModule 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 informationArtificial 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 informationCSL465/603 - 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 informationSystem 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 informationIntroduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition
Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and
More informationMachine 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 informationarxiv: 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 informationModel 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 informationQuickStroke: 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 informationSemantic 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 informationCS 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 informationProbabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview
More informationLecture 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 informationExploration. CS : Deep Reinforcement Learning Sergey Levine
Exploration CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 4 due on Wednesday 2. Project proposal feedback sent Today s Lecture 1. What is exploration? Why is it a problem?
More informationSoftprop: 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 informationA 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 informationAssignment 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 informationUnsupervised 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 informationDeep 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 informationAutoregressive 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 informationLearning 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 informationLecture 1: Basic Concepts of Machine Learning
Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010
More informationAxiom 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 informationHIERARCHICAL 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 informationChallenges in Deep Reinforcement Learning. Sergey Levine UC Berkeley
Challenges in Deep Reinforcement Learning Sergey Levine UC Berkeley Discuss some recent work in deep reinforcement learning Present a few major challenges Show some of our recent work toward tackling
More informationLearning From the Past with Experiment Databases
Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University
More informationKnowledge 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 informationSemi-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 informationarxiv: 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 informationWHEN THERE IS A mismatch between the acoustic
808 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 14, NO. 3, MAY 2006 Optimization of Temporal Filters for Constructing Robust Features in Speech Recognition Jeih-Weih Hung, Member,
More informationISFA2008U_120 A SCHEDULING REINFORCEMENT LEARNING ALGORITHM
Proceedings of 28 ISFA 28 International Symposium on Flexible Automation Atlanta, GA, USA June 23-26, 28 ISFA28U_12 A SCHEDULING REINFORCEMENT LEARNING ALGORITHM Amit Gil, Helman Stern, Yael Edan, and
More informationA survey of multi-view machine learning
Noname manuscript No. (will be inserted by the editor) A survey of multi-view machine learning Shiliang Sun Received: date / Accepted: date Abstract Multi-view learning or learning with multiple distinct
More informationIntroduction to Simulation
Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /
More informationADVANCED 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 informationCourse 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 informationSemi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration
INTERSPEECH 2013 Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration Yan Huang, Dong Yu, Yifan Gong, and Chaojun Liu Microsoft Corporation, One
More informationCultivating 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 informationSemi-Supervised Face Detection
Semi-Supervised Face Detection Nicu Sebe, Ira Cohen 2, Thomas S. Huang 3, Theo Gevers Faculty of Science, University of Amsterdam, The Netherlands 2 HP Research Labs, USA 3 Beckman Institute, University
More informationHuman 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 informationAnalysis 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 informationOCR for Arabic using SIFT Descriptors With Online Failure Prediction
OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,
More informationA 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 informationReinforcement Learning by Comparing Immediate Reward
Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate
More informationA 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 informationTruth Inference in Crowdsourcing: Is the Problem Solved?
Truth Inference in Crowdsourcing: Is the Problem Solved? Yudian Zheng, Guoliang Li #, Yuanbing Li #, Caihua Shan, Reynold Cheng # Department of Computer Science, Tsinghua University Department of Computer
More informationAttributed 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 informationarxiv: v2 [cs.cv] 30 Mar 2017
Domain Adaptation for Visual Applications: A Comprehensive Survey Gabriela Csurka arxiv:1702.05374v2 [cs.cv] 30 Mar 2017 Abstract The aim of this paper 1 is to give an overview of domain adaptation and
More informationMaximizing Learning Through Course Alignment and Experience with Different Types of Knowledge
Innov High Educ (2009) 34:93 103 DOI 10.1007/s10755-009-9095-2 Maximizing Learning Through Course Alignment and Experience with Different Types of Knowledge Phyllis Blumberg Published online: 3 February
More informationPREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES
PREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES Po-Sen Huang, Kshitiz Kumar, Chaojun Liu, Yifan Gong, Li Deng Department of Electrical and Computer Engineering,
More informationarxiv: v1 [cs.cv] 2 Jun 2017
Temporal Action Labeling using Action Sets Alexander Richard, Hilde Kuehne, Juergen Gall University of Bonn, Germany {richard,kuehne,gall}@iai.uni-bonn.de arxiv:1706.00699v1 [cs.cv] 2 Jun 2017 Abstract
More informationSEMI-SUPERVISED ENSEMBLE DNN ACOUSTIC MODEL TRAINING
SEMI-SUPERVISED ENSEMBLE DNN ACOUSTIC MODEL TRAINING Sheng Li 1, Xugang Lu 2, Shinsuke Sakai 1, Masato Mimura 1 and Tatsuya Kawahara 1 1 School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501,
More informationTwitter 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 informationMulti-tasks Deep Learning Model for classifying MRI images of AD/MCI Patients
Multi-tasks Deep Learning Model for classifying MRI images of AD/MCI Patients S.Sambath Kumar 1, Dr M. Nandhini 2, 1 Research scholar, 2 Assistant Professor 1,2 Department of Computer Science, Pondicherry
More informationUsing Deep Convolutional Neural Networks in Monte Carlo Tree Search
Using Deep Convolutional Neural Networks in Monte Carlo Tree Search Tobias Graf (B) and Marco Platzner University of Paderborn, Paderborn, Germany tobiasg@mail.upb.de, platzner@upb.de Abstract. Deep Convolutional
More informationEvolutive 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 informationFF+FPG: Guiding a Policy-Gradient Planner
FF+FPG: Guiding a Policy-Gradient Planner Olivier Buffet LAAS-CNRS University of Toulouse Toulouse, France firstname.lastname@laas.fr Douglas Aberdeen National ICT australia & The Australian National University
More informationRobot Learning Simultaneously a Task and How to Interpret Human Instructions
Robot Learning Simultaneously a Task and How to Interpret Human Instructions Jonathan Grizou, Manuel Lopes, Pierre-Yves Oudeyer To cite this version: Jonathan Grizou, Manuel Lopes, Pierre-Yves Oudeyer.
More informationDual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-6) Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors Sang-Woo Lee,
More informationDeep Facial Action Unit Recognition from Partially Labeled Data
Deep Facial Action Unit Recognition from Partially Labeled Data Shan Wu 1, Shangfei Wang,1, Bowen Pan 1, and Qiang Ji 2 1 University of Science and Technology of China, Hefei, Anhui, China 2 Rensselaer
More informationPhonetic- and Speaker-Discriminant Features for Speaker Recognition. Research Project
Phonetic- and Speaker-Discriminant Features for Speaker Recognition by Lara Stoll Research Project Submitted to the Department of Electrical Engineering and Computer Sciences, University of California
More informationAn empirical study of learning speed in backpropagation
Carnegie Mellon University Research Showcase @ CMU Computer Science Department School of Computer Science 1988 An empirical study of learning speed in backpropagation networks Scott E. Fahlman Carnegie
More informationThe Good Judgment Project: A large scale test of different methods of combining expert predictions
The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania
More informationTesting A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA
Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing a Moving Target How Do We Test Machine Learning Systems? Peter Varhol, Technology
More informationarxiv: 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 informationSpeech Emotion Recognition Using Support Vector Machine
Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,
More informationAMULTIAGENT system [1] can be defined as a group of
156 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART C: APPLICATIONS AND REVIEWS, VOL. 38, NO. 2, MARCH 2008 A Comprehensive Survey of Multiagent Reinforcement Learning Lucian Buşoniu, Robert Babuška,
More informationCalibration of Confidence Measures in Speech Recognition
Submitted to IEEE Trans on Audio, Speech, and Language, July 2010 1 Calibration of Confidence Measures in Speech Recognition Dong Yu, Senior Member, IEEE, Jinyu Li, Member, IEEE, Li Deng, Fellow, IEEE
More informationTime 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 informationIterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages
Iterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages Nuanwan Soonthornphisaj 1 and Boonserm Kijsirikul 2 Machine Intelligence and Knowledge Discovery Laboratory Department of Computer
More informationOn Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC
On Human Computer Interaction, HCI Dr. Saif al Zahir Electrical and Computer Engineering Department UBC Human Computer Interaction HCI HCI is the study of people, computer technology, and the ways these
More informationA Reinforcement Learning Variant for Control Scheduling
A Reinforcement Learning Variant for Control Scheduling Aloke Guha Honeywell Sensor and System Development Center 3660 Technology Drive Minneapolis MN 55417 Abstract We present an algorithm based on reinforcement
More informationINPE 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 informationCorrective Feedback and Persistent Learning for Information Extraction
Corrective Feedback and Persistent Learning for Information Extraction Aron Culotta a, Trausti Kristjansson b, Andrew McCallum a, Paul Viola c a Dept. of Computer Science, University of Massachusetts,
More informationSummarizing Answers in Non-Factoid Community Question-Answering
Summarizing Answers in Non-Factoid Community Question-Answering Hongya Song Zhaochun Ren Shangsong Liang hongya.song.sdu@gmail.com zhaochun.ren@ucl.ac.uk shangsong.liang@ucl.ac.uk Piji Li Jun Ma Maarten
More informationarxiv: 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 informationGeorgetown 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 informationAnalysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 2, Ver.1 (Mar - Apr.2015), PP 55-61 www.iosrjournals.org Analysis of Emotion
More informationAn 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 informationExperiment Databases: Towards an Improved Experimental Methodology in Machine Learning
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning Hendrik Blockeel and Joaquin Vanschoren Computer Science Dept., K.U.Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium
More informationarxiv: v2 [cs.ro] 3 Mar 2017
Learning Feedback Terms for Reactive Planning and Control Akshara Rai 2,3,, Giovanni Sutanto 1,2,, Stefan Schaal 1,2 and Franziska Meier 1,2 arxiv:1610.03557v2 [cs.ro] 3 Mar 2017 Abstract With the advancement
More informationRobust Speech Recognition using DNN-HMM Acoustic Model Combining Noise-aware training with Spectral Subtraction
INTERSPEECH 2015 Robust Speech Recognition using DNN-HMM Acoustic Model Combining Noise-aware training with Spectral Subtraction Akihiro Abe, Kazumasa Yamamoto, Seiichi Nakagawa Department of Computer
More informationDistributed 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 informationRover Races Grades: 3-5 Prep Time: ~45 Minutes Lesson Time: ~105 minutes
Rover Races Grades: 3-5 Prep Time: ~45 Minutes Lesson Time: ~105 minutes WHAT STUDENTS DO: Establishing Communication Procedures Following Curiosity on Mars often means roving to places with interesting
More informationAlgebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview
Algebra 1, Quarter 3, Unit 3.1 Line of Best Fit Overview Number of instructional days 6 (1 day assessment) (1 day = 45 minutes) Content to be learned Analyze scatter plots and construct the line of best
More informationA Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval
A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval Yelong Shen Microsoft Research Redmond, WA, USA yeshen@microsoft.com Xiaodong He Jianfeng Gao Li Deng Microsoft Research
More informationSwitchboard Language Model Improvement with Conversational Data from Gigaword
Katholieke Universiteit Leuven Faculty of Engineering Master in Artificial Intelligence (MAI) Speech and Language Technology (SLT) Switchboard Language Model Improvement with Conversational Data from Gigaword
More informationOPTIMIZATINON 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 informationarxiv: v2 [stat.ml] 30 Apr 2016 ABSTRACT
UNSUPERVISED AND SEMI-SUPERVISED LEARNING WITH CATEGORICAL GENERATIVE ADVERSARIAL NETWORKS Jost Tobias Springenberg University of Freiburg 79110 Freiburg, Germany springj@cs.uni-freiburg.de arxiv:1511.06390v2
More informationPOS 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 informationECE-492 SENIOR ADVANCED DESIGN PROJECT
ECE-492 SENIOR ADVANCED DESIGN PROJECT Meeting #3 1 ECE-492 Meeting#3 Q1: Who is not on a team? Q2: Which students/teams still did not select a topic? 2 ENGINEERING DESIGN You have studied a great deal
More informationApplications of data mining algorithms to analysis of medical data
Master Thesis Software Engineering Thesis no: MSE-2007:20 August 2007 Applications of data mining algorithms to analysis of medical data Dariusz Matyja School of Engineering Blekinge Institute of Technology
More informationExperiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling
Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Notebook for PAN at CLEF 2013 Andrés Alfonso Caurcel Díaz 1 and José María Gómez Hidalgo 2 1 Universidad
More informationA 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 informationWord 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 informationContinual Curiosity-Driven Skill Acquisition from High-Dimensional Video Inputs for Humanoid Robots
Continual Curiosity-Driven Skill Acquisition from High-Dimensional Video Inputs for Humanoid Robots Varun Raj Kompella, Marijn Stollenga, Matthew Luciw, Juergen Schmidhuber The Swiss AI Lab IDSIA, USI
More informationarxiv:submit/ [cs.cv] 2 Aug 2017
Associative Domain Adaptation Philip Haeusser 1,2 haeusser@in.tum.de Thomas Frerix 1 Alexander Mordvintsev 2 thomas.frerix@tum.de moralex@google.com 1 Dept. of Informatics, TU Munich 2 Google, Inc. Daniel
More information