Motivation Dropout Fast Dropout Maxout References. Dropout. Auston Sterling. January 26, 2016
|
|
- Wesley Roberts
- 6 years ago
- Views:
Transcription
1 Dropout Auston Sterling January 26, 2016
2 Outline Motivation Dropout Fast Dropout Maxout
3 Co-adaptation Each unit in a neural network should ideally compute one complete feature. Since units are trained together, multiple units may co-adapt, becoming dependent on one another to compute a feature This is sub-optimal, requiring more computation and causing overfitting
4 Co-adaptation 1 Which is preferable? 1 Srivastava et al., Dropout: A Simple Way to Prevent Neural Networks from Overfitting.
5 Model Combination We can reduce overfitting by combining the outputs of many different neural nets Best to train on different subsets of the data so that, while each may overfit to its subset, the combined models have a broader view This can be prohibitively expensive and requires large amounts of data
6 Sexual Reproduction Genes taken from either of two parents Each gene must be useful by itself; no guarantee that dependent genes will also make it through Specialized genes make it easy to incorporate beneficial new ones
7 Dropout 2 For each step of training, set the output of each unit to 0 with probability p. Best results with p 0.5 for hidden units and p close to 1 for inputs When testing, use all units but multiply weights by p That s it! 2 Hinton et al., Improving neural networks by preventing co-adaptation of feature detectors.
8 Dropout Notes Constrain L2 norm of weight vector for each unit (max-norm regularization), use a large learning rate The final trained network (if using softmax output) is exactly equivalent to the geometric mean of the probability distributions over labels predicted by all 2 N networks
9 Dropout results
10 Dropout results
11 Dropout is a Monte Carlo process, sampling the 2 N masks Can the process be approximated without requiring so much sampling? If z is the mask and w is weights, Y(z) = w T D z x = m i w i x i z i tends to a normal distribution Approximate Y(z) with a Gaussian and sample to compute gradients Fast Dropout Training 3 3 Wang and Manning, Fast dropout training.
12 Fast Dropout Results
13 Maxout Networks 4 Alternative activation function: h i (x) = max j [1,k] xt W i,j + b i,j Can approximate other activations Universal approximator 4 Goodfellow et al., Maxout networks.
14 Maxout and Dropout Dropout is exact model averaging for softmax, but also for multiple linear layers Authors claim linear operations with max works particularly well with dropout
15 Bibliography Goodfellow, Ian J et al. Maxout networks. In: arxiv preprint arxiv: (2013). Hinton, Geoffrey E. et al. Improving neural networks by preventing co-adaptation of feature detectors. In: CoRR abs/ (2012). URL: Srivastava, Nitish et al. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. In: Journal of Machine Learning Research 15 (2014), pp URL: Wang, Sida and Christopher Manning. Fast dropout training. In: Proceedings of the 30th International Conference on Machine Learning (ICML-13). Ed. by Sanjoy Dasgupta and David Mcallester. Vol JMLR Workshop and Conference Proceedings, May 2013, pp URL:
Weighted Convolutional Neural Network. Ensemble.
Weighted Convolutional Neural Network Ensemble Xavier Frazão and Luís A. Alexandre Dept. of Informatics, Univ. Beira Interior and Instituto de Telecomunicações Covilhã, Portugal xavierfrazao@gmail.com
More informationGroupout: A Way to Regularize Deep Convolutional Neural Network
Groupout: A Way to Regularize Deep Convolutional Neural Network Eunbyung Park Department of Computer Science University of North Carolina at Chapel Hill eunbyung@cs.unc.edu Abstract Groupout is a new technique
More informationDeep Learning Workshop. Nov. 20, 2015 Andrew Fishberg, Rowan Zellers
Deep Learning Workshop Nov. 20, 2015 Andrew Fishberg, Rowan Zellers Why deep learning? The ImageNet Challenge Goal: image classification with 1000 categories Top 5 error rate of 15%. Krizhevsky, Alex,
More informationDropout. Sargur N. Srihari This is part of lecture slides on Deep Learning:
Dropout Sargur N. srihari@buffalo.edu This is part of lecture slides on Deep Learning: http://www.cedar.buffalo.edu/~srihari/cse676 1 Regularization Strategies 1. Parameter Norm Penalties 2. Norm Penalties
More informationDeep Learning for Computer Vision
Deep Learning for Computer Vision Lecture 7: Universal Approximation Theorem, More Hidden Units, Multi-Class Classifiers, Softmax, and Regularization Peter Belhumeur Computer Science Columbia University
More informationStochastic Function Norm Regularization of DNNs
Stochastic Function Norm Regularization of DNNs Amal Rannen Triki Dept. of Computational Science and Engineering Yonsei University Seoul, South Korea amal.rannen@yonsei.ac.kr Matthew B. Blaschko Center
More informationDeep Learning With Noise
Deep Learning With Noise Yixin Luo Computer Science Department Carnegie Mellon University yixinluo@cs.cmu.edu Fan Yang Department of Mathematical Sciences Carnegie Mellon University fanyang1@andrew.cmu.edu
More informationComparing Dropout Nets to Sum-Product Networks for Predicting Molecular Activity
000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050
More informationEnd-To-End Spam Classification With Neural Networks
End-To-End Spam Classification With Neural Networks Christopher Lennan, Bastian Naber, Jan Reher, Leon Weber 1 Introduction A few years ago, the majority of the internet s network traffic was due to spam
More informationDeep Learning with Tensorflow AlexNet
Machine Learning and Computer Vision Group Deep Learning with Tensorflow http://cvml.ist.ac.at/courses/dlwt_w17/ AlexNet Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton, "Imagenet classification
More informationDeep Learning for Computer Vision II
IIIT Hyderabad Deep Learning for Computer Vision II C. V. Jawahar Paradigm Shift Feature Extraction (SIFT, HoG, ) Part Models / Encoding Classifier Sparrow Feature Learning Classifier Sparrow L 1 L 2 L
More informationDeep Model Compression
Deep Model Compression Xin Wang Oct.31.2016 Some of the contents are borrowed from Hinton s and Song s slides. Two papers Distilling the Knowledge in a Neural Network by Geoffrey Hinton et al What s the
More informationCS489/698: Intro to ML
CS489/698: Intro to ML Lecture 14: Training of Deep NNs Instructor: Sun Sun 1 Outline Activation functions Regularization Gradient-based optimization 2 Examples of activation functions 3 5/28/18 Sun Sun
More informationFrom Maxout to Channel-Out: Encoding Information on Sparse Pathways
From Maxout to Channel-Out: Encoding Information on Sparse Pathways Qi Wang and Joseph JaJa Department of Electrical and Computer Engineering and, University of Maryland Institute of Advanced Computer
More informationImageNet Classification with Deep Convolutional Neural Networks
ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky Ilya Sutskever Geoffrey Hinton University of Toronto Canada Paper with same name to appear in NIPS 2012 Main idea Architecture
More informationResearch on Pruning Convolutional Neural Network, Autoencoder and Capsule Network
Research on Pruning Convolutional Neural Network, Autoencoder and Capsule Network Tianyu Wang Australia National University, Colledge of Engineering and Computer Science u@anu.edu.au Abstract. Some tasks,
More informationSEMANTIC COMPUTING. Lecture 9: Deep Learning: Recurrent Neural Networks (RNNs) TU Dresden, 21 December 2018
SEMANTIC COMPUTING Lecture 9: Deep Learning: Recurrent Neural Networks (RNNs) Dagmar Gromann International Center For Computational Logic TU Dresden, 21 December 2018 Overview Handling Overfitting Recurrent
More informationDropConnect Regularization Method with Sparsity Constraint for Neural Networks
Chinese Journal of Electronics Vol.25, No.1, Jan. 2016 DropConnect Regularization Method with Sparsity Constraint for Neural Networks LIAN Zifeng 1,JINGXiaojun 1, WANG Xiaohan 2, HUANG Hai 1, TAN Youheng
More informationContextual Dropout. Sam Fok. Abstract. 1. Introduction. 2. Background and Related Work
Contextual Dropout Finding subnets for subtasks Sam Fok samfok@stanford.edu Abstract The feedforward networks widely used in classification are static and have no means for leveraging information about
More informationarxiv: v2 [cs.cv] 26 Jan 2018
DIRACNETS: TRAINING VERY DEEP NEURAL NET- WORKS WITHOUT SKIP-CONNECTIONS Sergey Zagoruyko, Nikos Komodakis Université Paris-Est, École des Ponts ParisTech Paris, France {sergey.zagoruyko,nikos.komodakis}@enpc.fr
More informationAdversarial Examples and Adversarial Training. Ian Goodfellow, Staff Research Scientist, Google Brain CS 231n, Stanford University,
Adversarial Examples and Adversarial Training Ian Goodfellow, Staff Research Scientist, Google Brain CS 231n, Stanford University, 2017-05-30 Overview What are adversarial examples? Why do they happen?
More informationNeural Networks for Machine Learning. Lecture 15a From Principal Components Analysis to Autoencoders
Neural Networks for Machine Learning Lecture 15a From Principal Components Analysis to Autoencoders Geoffrey Hinton Nitish Srivastava, Kevin Swersky Tijmen Tieleman Abdel-rahman Mohamed Principal Components
More informationFast-Lipschitz Optimization
Fast-Lipschitz Optimization DREAM Seminar Series University of California at Berkeley September 11, 2012 Carlo Fischione ACCESS Linnaeus Center, Electrical Engineering KTH Royal Institute of Technology
More informationImproving the way neural networks learn Srikumar Ramalingam School of Computing University of Utah
Improving the way neural networks learn Srikumar Ramalingam School of Computing University of Utah Reference Most of the slides are taken from the third chapter of the online book by Michael Nielson: neuralnetworksanddeeplearning.com
More informationAlternatives to Direct Supervision
CreativeAI: Deep Learning for Graphics Alternatives to Direct Supervision Niloy Mitra Iasonas Kokkinos Paul Guerrero Nils Thuerey Tobias Ritschel UCL UCL UCL TUM UCL Timetable Theory and Basics State of
More informationConvolutional Neural Networks. Computer Vision Jia-Bin Huang, Virginia Tech
Convolutional Neural Networks Computer Vision Jia-Bin Huang, Virginia Tech Today s class Overview Convolutional Neural Network (CNN) Training CNN Understanding and Visualizing CNN Image Categorization:
More informationResidual Networks And Attention Models. cs273b Recitation 11/11/2016. Anna Shcherbina
Residual Networks And Attention Models cs273b Recitation 11/11/2016 Anna Shcherbina Introduction to ResNets Introduced in 2015 by Microsoft Research Deep Residual Learning for Image Recognition (He, Zhang,
More informationarxiv: v1 [cs.cv] 9 Nov 2015
Batch-normalized Maxout Network in Network arxiv:1511.02583v1 [cs.cv] 9 Nov 2015 Jia-Ren Chang Department of Computer Science National Chiao Tung University, Hsinchu, Taiwan followwar.cs00g@nctu.edu.tw
More informationDeep Neural Networks:
Deep Neural Networks: Part II Convolutional Neural Network (CNN) Yuan-Kai Wang, 2016 Web site of this course: http://pattern-recognition.weebly.com source: CNN for ImageClassification, by S. Lazebnik,
More informationDeep Learning. Volker Tresp Summer 2014
Deep Learning Volker Tresp Summer 2014 1 Neural Network Winter and Revival While Machine Learning was flourishing, there was a Neural Network winter (late 1990 s until late 2000 s) Around 2010 there
More informationSlides credited from Dr. David Silver & Hung-Yi Lee
Slides credited from Dr. David Silver & Hung-Yi Lee Review Reinforcement Learning 2 Reinforcement Learning RL is a general purpose framework for decision making RL is for an agent with the capacity to
More informationBackpropagation and Neural Networks. Lecture 4-1
Lecture 4: Backpropagation and Neural Networks Lecture 4-1 Administrative Assignment 1 due Thursday April 20, 11:59pm on Canvas Lecture 4-2 Administrative Project: TA specialities and some project ideas
More informationOverall Description. Goal: to improve spatial invariance to the input data. Translation, Rotation, Scale, Clutter, Elastic
Philippe Giguère Overall Description Goal: to improve spatial invariance to the input data Translation, Rotation, Scale, Clutter, Elastic How: add a learnable module which explicitly manipulate spatially
More informationSupplementary material for Analyzing Filters Toward Efficient ConvNet
Supplementary material for Analyzing Filters Toward Efficient Net Takumi Kobayashi National Institute of Advanced Industrial Science and Technology, Japan takumi.kobayashi@aist.go.jp A. Orthonormal Steerable
More informationCOMP 551 Applied Machine Learning Lecture 16: Deep Learning
COMP 551 Applied Machine Learning Lecture 16: Deep Learning Instructor: Ryan Lowe (ryan.lowe@cs.mcgill.ca) Slides mostly by: Class web page: www.cs.mcgill.ca/~hvanho2/comp551 Unless otherwise noted, all
More informationStochastic Gradient Descent Algorithm in the Computational Network Toolkit
Stochastic Gradient Descent Algorithm in the Computational Network Toolkit Brian Guenter, Dong Yu, Adam Eversole, Oleksii Kuchaiev, Michael L. Seltzer Microsoft Corporation One Microsoft Way Redmond, WA
More informationUnsupervised Learning
Deep Learning for Graphics Unsupervised Learning Niloy Mitra Iasonas Kokkinos Paul Guerrero Vladimir Kim Kostas Rematas Tobias Ritschel UCL UCL/Facebook UCL Adobe Research U Washington UCL Timetable Niloy
More informationRegularization. EE807: Recent Advances in Deep Learning Lecture 3. Slide made by Jongheon Jeong and Insu Han KAIST EE
Regularization EE807: Recent Advances in Deep Learning Lecture 3 Slide made by Jongheon Jeong and Insu Han KAIST EE What is regularization? Any modification we make to a learning algorithm that is intended
More informationAn Exploration of Computer Vision Techniques for Bird Species Classification
An Exploration of Computer Vision Techniques for Bird Species Classification Anne L. Alter, Karen M. Wang December 15, 2017 Abstract Bird classification, a fine-grained categorization task, is a complex
More informationReplay spoofing detection system for automatic speaker verification using multi-task learning of noise classes
Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes Hye-Jin Shim shimhz6.6@gmail.com Sung-Hyun Yoon ysh901108@naver.com Jee-Weon Jung jeewon.leo.jung@gmail.com
More informationConvolutional Neural Network for Facial Expression Recognition
Convolutional Neural Network for Facial Expression Recognition Liyuan Zheng Department of Electrical Engineering University of Washington liyuanz8@uw.edu Shifeng Zhu Department of Electrical Engineering
More informationNon-rigid body Object Tracking using Fuzzy Neural System based on Multiple ROIs and Adaptive Motion Frame Method
Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 2009 Non-rigid body Object Tracking using Fuzzy Neural System based on Multiple ROIs
More informationarxiv: v5 [cs.lg] 23 Sep 2015
TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS Matthieu Courbariaux & Jean-Pierre David École Polytechnique de Montréal {matthieu.courbariaux,jean-pierre.david}@polymtl.ca arxiv:1412.7024v5
More informationarxiv: v3 [stat.ml] 15 Nov 2017
Reuben Feinman 1 Ryan R. Curtin 1 Saurabh Shintre 2 Andrew B. Gardner 1 arxiv:1703.00410v3 [stat.ml] 15 Nov 2017 Abstract Deep neural networks (DNNs) are powerful nonlinear architectures that are known
More informationDeep Neural Network Acceleration Framework Under Hardware Uncertainty
Deep Neural Network Acceleration Framework Under Hardware Uncertainty Mohsen Imani, Pushen Wang, and Tajana Rosing Computer Science and Engineering, UC San Diego, La Jolla, CA 92093, USA {moimani, puw001,
More informationConvolutional Neural Networks
Lecturer: Barnabas Poczos Introduction to Machine Learning (Lecture Notes) Convolutional Neural Networks Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications.
More informationAutomated Crystal Structure Identification from X-ray Diffraction Patterns
Automated Crystal Structure Identification from X-ray Diffraction Patterns Rohit Prasanna (rohitpr) and Luca Bertoluzzi (bertoluz) CS229: Final Report 1 Introduction X-ray diffraction is a commonly used
More informationModel validation T , , Heli Hiisilä
Model validation T-61.6040, 03.10.2006, Heli Hiisilä Testing Neural Models: How to Use Re-Sampling Techniques? A. Lendasse & Fast bootstrap methodology for model selection, A. Lendasse, G. Simon, V. Wertz,
More informationEE 511 Neural Networks
Slides adapted from Ali Farhadi, Mari Ostendorf, Pedro Domingos, Carlos Guestrin, and Luke Zettelmoyer, Andrei Karpathy EE 511 Neural Networks Instructor: Hanna Hajishirzi hannaneh@washington.edu Computational
More informationDomain-Aware Sentiment Classification with GRUs and CNNs
Domain-Aware Sentiment Classification with GRUs and CNNs Guangyuan Piao 1(B) and John G. Breslin 2 1 Insight Centre for Data Analytics, Data Science Institute, National University of Ireland Galway, Galway,
More informationShow, Discriminate, and Tell: A Discriminatory Image Captioning Model with Deep Neural Networks
Show, Discriminate, and Tell: A Discriminatory Image Captioning Model with Deep Neural Networks Zelun Luo Department of Computer Science Stanford University zelunluo@stanford.edu Te-Lin Wu Department of
More informationReal-time convolutional networks for sonar image classification in low-power embedded systems
Real-time convolutional networks for sonar image classification in low-power embedded systems Matias Valdenegro-Toro Ocean Systems Laboratory - School of Engineering & Physical Sciences Heriot-Watt University,
More informationDynamic Routing Using Inter Capsule Routing Protocol Between Capsules
2018 UKSim-AMSS 20th International Conference on Modelling & Simulation Dynamic Routing Using Inter Capsule Routing Protocol Between Capsules Sanjib Kumar Sahu GGS Indraprastha University Delhi, India,
More informationarxiv: v2 [cs.cv] 20 Oct 2018
CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces Liheng Zhang, Marzieh Edraki, and Guo-Jun Qi arxiv:1805.07621v2 [cs.cv] 20 Oct 2018 Laboratory for MAchine Perception
More informationmodel order p weights The solution to this optimization problem is obtained by solving the linear system
CS 189 Introduction to Machine Learning Fall 2017 Note 3 1 Regression and hyperparameters Recall the supervised regression setting in which we attempt to learn a mapping f : R d R from labeled examples
More informationLearning Transferable Features with Deep Adaptation Networks
Learning Transferable Features with Deep Adaptation Networks Mingsheng Long, Yue Cao, Jianmin Wang, Michael I. Jordan Presented by Changyou Chen October 30, 2015 1 Changyou Chen Learning Transferable Features
More informationDeep Learning & Neural Networks
Deep Learning & Neural Networks Machine Learning CSE4546 Sham Kakade University of Washington November 29, 2016 Sham Kakade 1 Announcements: HW4 posted Poster Session Thurs, Dec 8 Today: Review: EM Neural
More information10703 Deep Reinforcement Learning and Control
10703 Deep Reinforcement Learning and Control Russ Salakhutdinov Machine Learning Department rsalakhu@cs.cmu.edu Policy Gradient II Used Materials Disclaimer: Much of the material and slides for this lecture
More informationGeneralized Inverse Reinforcement Learning
Generalized Inverse Reinforcement Learning James MacGlashan Cogitai, Inc. james@cogitai.com Michael L. Littman mlittman@cs.brown.edu Nakul Gopalan ngopalan@cs.brown.edu Amy Greenwald amy@cs.brown.edu Abstract
More information3D model classification using convolutional neural network
3D model classification using convolutional neural network JunYoung Gwak Stanford jgwak@cs.stanford.edu Abstract Our goal is to classify 3D models directly using convolutional neural network. Most of existing
More informationTiny ImageNet Visual Recognition Challenge
Tiny ImageNet Visual Recognition Challenge Ya Le Department of Statistics Stanford University yle@stanford.edu Xuan Yang Department of Electrical Engineering Stanford University xuany@stanford.edu Abstract
More informationJOINT INTENT DETECTION AND SLOT FILLING USING CONVOLUTIONAL NEURAL NETWORKS. Puyang Xu, Ruhi Sarikaya. Microsoft Corporation
JOINT INTENT DETECTION AND SLOT FILLING USING CONVOLUTIONAL NEURAL NETWORKS Puyang Xu, Ruhi Sarikaya Microsoft Corporation ABSTRACT We describe a joint model for intent detection and slot filling based
More informationDeep Neural Networks for Recognizing Online Handwritten Mathematical Symbols
Deep Neural Networks for Recognizing Online Handwritten Mathematical Symbols Hai Dai Nguyen 1, Anh Duc Le 2 and Masaki Nakagawa 3 Tokyo University of Agriculture and Technology 2-24-16 Nakacho, Koganei-shi,
More informationMachine Learning. The Breadth of ML Neural Networks & Deep Learning. Marc Toussaint. Duy Nguyen-Tuong. University of Stuttgart
Machine Learning The Breadth of ML Neural Networks & Deep Learning Marc Toussaint University of Stuttgart Duy Nguyen-Tuong Bosch Center for Artificial Intelligence Summer 2017 Neural Networks Consider
More informationarxiv: v3 [cs.lg] 23 Jan 2018
Marco Singh * 1 Akshay Pai * 2 3 arxiv:1801.02642v3 [cs.lg] 23 Jan 2018 Abstract Despite all the success that deep neural networks have seen in classifying certain datasets, the challenge of finding optimal
More informationSummary: A Tutorial on Learning With Bayesian Networks
Summary: A Tutorial on Learning With Bayesian Networks Markus Kalisch May 5, 2006 We primarily summarize [4]. When we think that it is appropriate, we comment on additional facts and more recent developments.
More informationTraining Convolutional Neural Networks for Translational Invariance on SAR ATR
Downloaded from orbit.dtu.dk on: Mar 28, 2019 Training Convolutional Neural Networks for Translational Invariance on SAR ATR Malmgren-Hansen, David; Engholm, Rasmus ; Østergaard Pedersen, Morten Published
More informationDynamic Routing Between Capsules
Report Explainable Machine Learning Dynamic Routing Between Capsules Author: Michael Dorkenwald Supervisor: Dr. Ullrich Köthe 28. Juni 2018 Inhaltsverzeichnis 1 Introduction 2 2 Motivation 2 3 CapusleNet
More informationarxiv: v3 [stat.ml] 20 Feb 2013
arxiv:1302.4389v3 [stat.ml] 20 Feb 2013 Ian J. Goodfellow goodfeli@iro.umontreal.ca David Warde-Farley wardefar@iro.umontreal.ca Mehdi Mirza mirzamom@iro.umontreal.ca Aaron Courville aaron.courville@umontreal.ca
More informationCharacter Recognition from Google Street View Images
Character Recognition from Google Street View Images Indian Institute of Technology Course Project Report CS365A By Ritesh Kumar (11602) and Srikant Singh (12729) Under the guidance of Professor Amitabha
More information3D-CNN and SVM for Multi-Drug Resistance Detection
3D-CNN and SVM for Multi-Drug Resistance Detection Imane Allaouzi, Badr Benamrou, Mohamed Benamrou and Mohamed Ben Ahmed Abdelmalek Essaâdi University Faculty of Sciences and Techniques, Tangier, Morocco
More informationConvolution Neural Network for Traditional Chinese Calligraphy Recognition
Convolution Neural Network for Traditional Chinese Calligraphy Recognition Boqi Li Mechanical Engineering Stanford University boqili@stanford.edu Abstract script. Fig. 1 shows examples of the same TCC
More informationGlobal Optimality in Neural Network Training
Global Optimality in Neural Network Training Benjamin D. Haeffele and René Vidal Johns Hopkins University, Center for Imaging Science. Baltimore, USA Questions in Deep Learning Architecture Design Optimization
More informationCost-alleviative Learning for Deep Convolutional Neural Network-based Facial Part Labeling
[DOI: 10.2197/ipsjtcva.7.99] Express Paper Cost-alleviative Learning for Deep Convolutional Neural Network-based Facial Part Labeling Takayoshi Yamashita 1,a) Takaya Nakamura 1 Hiroshi Fukui 1,b) Yuji
More informationAll You Want To Know About CNNs. Yukun Zhu
All You Want To Know About CNNs Yukun Zhu Deep Learning Deep Learning Image from http://imgur.com/ Deep Learning Image from http://imgur.com/ Deep Learning Image from http://imgur.com/ Deep Learning Image
More informationA Fast Learning Algorithm for Deep Belief Nets
A Fast Learning Algorithm for Deep Belief Nets Geoffrey E. Hinton, Simon Osindero Department of Computer Science University of Toronto, Toronto, Canada Yee-Whye Teh Department of Computer Science National
More informationDeep Neural Networks Optimization
Deep Neural Networks Optimization Creative Commons (cc) by Akritasa http://arxiv.org/pdf/1406.2572.pdf Slides from Geoffrey Hinton CSC411/2515: Machine Learning and Data Mining, Winter 2018 Michael Guerzhoy
More informationDeep neural networks II
Deep neural networks II May 31 st, 2018 Yong Jae Lee UC Davis Many slides from Rob Fergus, Svetlana Lazebnik, Jia-Bin Huang, Derek Hoiem, Adriana Kovashka, Why (convolutional) neural networks? State of
More informationCOS 513: Foundations of Probabilistic Modeling. Lecture 5
COS 513: Foundations of Probabilistic Modeling Young-suk Lee 1 Administrative Midterm report is due Oct. 29 th. Recitation is at 4:26pm in Friend 108. Lecture 5 R is a computer language for statistical
More informationDoes the Brain do Inverse Graphics?
Does the Brain do Inverse Graphics? Geoffrey Hinton, Alex Krizhevsky, Navdeep Jaitly, Tijmen Tieleman & Yichuan Tang Department of Computer Science University of Toronto The representation used by the
More informationWhy equivariance is better than premature invariance
1 Why equivariance is better than premature invariance Geoffrey Hinton Canadian Institute for Advanced Research & Department of Computer Science University of Toronto with contributions from Sida Wang
More informationMulti-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks Ian J. Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, Vinay Shet Google Inc., Mountain View,
More informationStacked Denoising Autoencoders for Face Pose Normalization
Stacked Denoising Autoencoders for Face Pose Normalization Yoonseop Kang 1, Kang-Tae Lee 2,JihyunEun 2, Sung Eun Park 2 and Seungjin Choi 1 1 Department of Computer Science and Engineering Pohang University
More informationKeras: Handwritten Digit Recognition using MNIST Dataset
Keras: Handwritten Digit Recognition using MNIST Dataset IIT PATNA January 31, 2018 1 / 30 OUTLINE 1 Keras: Introduction 2 Installing Keras 3 Keras: Building, Testing, Improving A Simple Network 2 / 30
More informationExemplar-Supported Generative Reproduction for Class Incremental Learning Supplementary Material
HE ET AL.: EXEMPLAR-SUPPORTED GENERATIVE REPRODUCTION 1 Exemplar-Supported Generative Reproduction for Class Incremental Learning Supplementary Material Chen He 1,2 chen.he@vipl.ict.ac.cn Ruiping Wang
More informationLearning from Data: Adaptive Basis Functions
Learning from Data: Adaptive Basis Functions November 21, 2005 http://www.anc.ed.ac.uk/ amos/lfd/ Neural Networks Hidden to output layer - a linear parameter model But adapt the features of the model.
More informationarxiv: v1 [cs.cv] 6 Jul 2016
arxiv:607.079v [cs.cv] 6 Jul 206 Deep CORAL: Correlation Alignment for Deep Domain Adaptation Baochen Sun and Kate Saenko University of Massachusetts Lowell, Boston University Abstract. Deep neural networks
More informationExploring Capsules. Binghui Peng Runzhou Tao Shunyu Yao IIIS, Tsinghua University {pbh15, trz15,
Exploring Capsules Binghui Peng Runzhou Tao Shunyu Yao IIIS, Tsinghua University {pbh15, trz15, yao-sy15}@mails.tsinghua.edu.cn 1 Introduction Nowadays, convolutional neural networks (CNNs) have received
More informationPractical Methodology. Lecture slides for Chapter 11 of Deep Learning Ian Goodfellow
Practical Methodology Lecture slides for Chapter 11 of Deep Learning www.deeplearningbook.org Ian Goodfellow 2016-09-26 What drives success in ML? Arcane knowledge of dozens of obscure algorithms? Mountains
More informationAdaptive Dropout Training for SVMs
Department of Computer Science and Technology Adaptive Dropout Training for SVMs Jun Zhu Joint with Ning Chen, Jingwei Zhuo, Jianfei Chen, Bo Zhang Tsinghua University ShanghaiTech Symposium on Data Science,
More informationGradient of the lower bound
Weakly Supervised with Latent PhD advisor: Dr. Ambedkar Dukkipati Department of Computer Science and Automation gaurav.pandey@csa.iisc.ernet.in Objective Given a training set that comprises image and image-level
More informationCEA LIST s participation to the Scalable Concept Image Annotation task of ImageCLEF 2015
CEA LIST s participation to the Scalable Concept Image Annotation task of ImageCLEF 2015 Etienne Gadeski, Hervé Le Borgne, and Adrian Popescu CEA, LIST, Laboratory of Vision and Content Engineering, France
More informationScalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters Jelena Luketina 1 Mathias Berglund 1 Klaus Greff 2 Tapani Raiko 1 1 Department of Computer Science, Aalto University, Finland
More informationPIXELCNN++: IMPROVING THE PIXELCNN WITH DISCRETIZED LOGISTIC MIXTURE LIKELIHOOD AND OTHER MODIFICATIONS
PIXELCNN++: IMPROVING THE PIXELCNN WITH DISCRETIZED LOGISTIC MIXTURE LIKELIHOOD AND OTHER MODIFICATIONS Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma {tim,karpathy,peter,dpkingma}@openai.com
More informationPart Localization by Exploiting Deep Convolutional Networks
Part Localization by Exploiting Deep Convolutional Networks Marcel Simon, Erik Rodner, and Joachim Denzler Computer Vision Group, Friedrich Schiller University of Jena, Germany www.inf-cv.uni-jena.de Abstract.
More informationOn the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units
On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units Zhibin Liao Gustavo Carneiro ARC Centre of Excellence for Robotic Vision University of Adelaide, Australia
More informationGRADIENT-BASED OPTIMIZATION OF NEURAL
Workshop track - ICLR 28 GRADIENT-BASED OPTIMIZATION OF NEURAL NETWORK ARCHITECTURE Will Grathwohl, Elliot Creager, Seyed Kamyar Seyed Ghasemipour, Richard Zemel Department of Computer Science University
More informationImplementation of Deep Convolutional Neural Net on a Digital Signal Processor
Implementation of Deep Convolutional Neural Net on a Digital Signal Processor Elaina Chai December 12, 2014 1. Abstract In this paper I will discuss the feasibility of an implementation of an algorithm
More informationRyerson University CP8208. Soft Computing and Machine Intelligence. Naive Road-Detection using CNNS. Authors: Sarah Asiri - Domenic Curro
Ryerson University CP8208 Soft Computing and Machine Intelligence Naive Road-Detection using CNNS Authors: Sarah Asiri - Domenic Curro April 24 2016 Contents 1 Abstract 2 2 Introduction 2 3 Motivation
More informationDeep Learning in Visual Recognition. Thanks Da Zhang for the slides
Deep Learning in Visual Recognition Thanks Da Zhang for the slides Deep Learning is Everywhere 2 Roadmap Introduction Convolutional Neural Network Application Image Classification Object Detection Object
More information