Deep Learning for Embedded Security Evaluation
|
|
- Edgar Richards
- 5 years ago
- Views:
Transcription
1 Deep Learning for Embedded Security Evaluation Emmanuel Prouff 1 1 Laboratoire de Sécurité des Composants, ANSSI, France April 2018, CISCO April 2018, CISCO E. Prouff 1/22
2 Contents 1. Context and Motivation 1.1 Illustration 1.2 Template Attacks 1.3 Countermeasures 2. A machine learning approach to classification 2.1 Introduction 2.2 The Underlying Classification Problem 2.3 Convolutional Neural Networks 2.4 Training of Models 3. Building a Community Around The Subject 3.1 ASCAD Open Data-Base 3.2 Leakage Characterization and Training 3.3 Results 4. Conclusions April 2018, CISCO E. Prouff 2/22
3 Probability distribution function (pdf) of Electromagnetic Emanations Cryptographic Processing with a secret k = 1. April 2018, CISCO E. Prouff 3/22
4 Probability distribution function (pdf) of Electromagnetic Emanations Cryptographic Processing with a secret k = 1. April 2018, CISCO E. Prouff 3/22
5 Probability distribution function (pdf) of Electromagnetic Emanations Cryptographic Processing with a secret k = 2. April 2018, CISCO E. Prouff 3/22
6 Probability distribution function (pdf) of Electromagnetic Emanations Cryptographic Processing with a secret k = 3. April 2018, CISCO E. Prouff 3/22
7 Probability distribution function (pdf) of Electromagnetic Emanations Cryptographic Processing with a secret k = 4. April 2018, CISCO E. Prouff 3/22
8 Context: Target Device Clone Device April 2018, CISCO E. Prouff 4/22
9 Context: Target Device Clone Device [On Clone Device] For every k estimate the pdf of X K = k. k = 1 k = 2 k = 3 k = 4 April 2018, CISCO E. Prouff 4/22
10 Context: Target Device Clone Device [On Clone Device] For every k estimate the pdf of X K = k. k = 1 k = 2 k = 3 k = 4 April 2018, CISCO E. Prouff 4/22
11 Context: Target Device Clone Device [On Clone Device] For every k estimate the pdf of X K = k. k = 1 k = 2 k = 3 [On Target Device] Estimate the pdf of X. k = 4 k =? April 2018, CISCO E. Prouff 4/22
12 Context: Target Device Clone Device [On Clone Device] For every k estimate the pdf of X K = k. k = 1 k = 2 k = 3 [On Target Device] Estimate the pdf of X. k = 4 k =? [Key-recovery] Compare the pdf estimations. April 2018, CISCO E. Prouff 4/22
13 Side Channel Attacks (Classical Approach) Notations X observation of the device behaviour (consumption, electro-magnetic emanation, timing, etc.) P public input of the processing Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X April 2018, CISCO E. Prouff 5/22
14 Side Channel Attacks (Classical Approach) Notations X observation of the device behaviour (consumption, electro-magnetic emanation, timing, etc.) P public input of the processing Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X Pr[Z X] April 2018, CISCO E. Prouff 5/22
15 Side Channel Attacks (Classical Approach) Notations X observation of the device behaviour (consumption, electro-magnetic emanation, timing, etc.) P public input of the processing Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X Pr[Z X] Template Attacks Profiling phase (using profiling traces under known Z) Attack phase (N attack traces x i, e.g. with known plaintexts p i ) Log-likelihood score for each key hypothesis k N d k = log Pr[ X = x i Z = f (p i, k)] i=1 April 2018, CISCO E. Prouff 5/22
16 Side Channel Attacks (Classical Approach) Notations X observation of the device behaviour (consumption, electro-magnetic emanation, timing, etc.) P public input of the processing Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X Pr[Z X] Template Attacks Profiling phase (using profiling traces under known Z) estimate Pr[ X Z = z] by simple distributions for each value of z Attack phase (N attack traces x i, e.g. with known plaintexts p i ) Log-likelihood score for each key hypothesis k N d k = log Pr[ X = x i Z = f (p i, k)] i=1 April 2018, CISCO E. Prouff 5/22
17 Side Channel Attacks (Classical Approach) Notations X observation of the device behaviour (consumption, electro-magnetic emanation, timing, etc.) P public input of the processing Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X Pr[Z X] Template Attacks Profiling phase (using profiling traces under known Z) estimate Pr[ X Z = z] for each value of z Attack phase (N attack traces x i, e.g. with known plaintexts p i ) Log-likelihood score for each key hypothesis k N d k = log Pr[ X = x i Z = f (p i, k)] i=1 April 2018, CISCO E. Prouff 5/22
18 Side Channel Attacks (Classical Approach) Notations X observation of the device behaviour (consumption, electro-magnetic emanation, timing, etc.) P public input of the processing Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X Pr[Z X] Template Attacks Profiling phase (using profiling traces under known Z) mandatory dimensionality reduction estimate Pr[ X Z = z] for each value of z Attack phase (N attack traces x i, e.g. with known plaintexts p i ) Log-likelihood score for each key hypothesis k N d k = log Pr[ X = x i Z = f (p i, k)] i=1 April 2018, CISCO E. Prouff 5/22
19 Side Channel Attacks (Classical Approach) Notations X observation of the device behaviour (consumption, electro-magnetic emanation, timing, etc.) P public input of the processing Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X Pr[Z X] Template Attacks Profiling phase (using profiling traces under known Z) manage de-synchronization problem mandatory dimensionality reduction estimate Pr[ X Z = z] for each value of z Attack phase (N attack traces x i, e.g. with known plaintexts p i ) Log-likelihood score for each key hypothesis k N d k = log Pr[ X = x i Z = f (p i, k)] i=1 April 2018, CISCO E. Prouff 5/22
20 Defensive Mechanisms Misaligning Countermeasures Random Delays, Clock Jittering,... In theory: assume to be insufficient to provide security In practice: one of the main issues for evaluators = Need for efficient resynchronization techniques April 2018, CISCO E. Prouff 6/22
21 Defensive Mechanisms Misaligning Countermeasures Random Delays, Clock Jittering,... In theory: assume to be insufficient to provide security In practice: one of the main issues for evaluators = Need for efficient resynchronization techniques Masking Countermeasure Each key-dependent internal state element is randomly split into 2 shares The crypto algorithm is adapted to always manipulate shares at times The adversary needs to recover information on the two shares to recover K = Need for efficient Methods to recover tuple of leakage samples that jointly depend on the target secret April 2018, CISCO E. Prouff 6/22
22 Contents 1. Context and Motivation 1.1 Illustration 1.2 Template Attacks 1.3 Countermeasures 2. A machine learning approach to classification 2.1 Introduction 2.2 The Underlying Classification Problem 2.3 Convolutional Neural Networks 2.4 Training of Models 3. Building a Community Around The Subject 3.1 ASCAD Open Data-Base 3.2 Leakage Characterization and Training 3.3 Results 4. Conclusions April 2018, CISCO E. Prouff 7/22
23 Motivating Conclusions An evaluator can spend time to adjust realignment and/or to find points of interest... try realignments based over other kind of patterns modify parameters... April 2018, CISCO E. Prouff 8/22
24 Motivating Conclusions An evaluator can spend time to adjust realignment and/or to find points of interest... try realignments based over other kind of patterns modify parameters......but no prior knowledge about informative patterns no way to evaluate realignment without launching the afterwards attack April 2018, CISCO E. Prouff 8/22
25 Motivating Conclusions An evaluator can spend time to adjust realignment and/or to find points of interest... try realignments based over other kind of patterns modify parameters......but no prior knowledge about informative patterns no way to evaluate realignment without launching the afterwards attack Now: preprocessing to prepare data make strong hypotheses on the statistical dependency characterization to extract information The proposed perspective: preprocessing to prepare data make strong hypotheses on the statistical dependency Train algorithms to directly extract information April 2018, CISCO E. Prouff 8/22
26 Side Channel Attacks Notations X side channel trace Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X Pr[Z X] Template Attacks Machine Learning Side Channel Attacks Profiling phase (using profiling traces under known Z) manage de-synchronization problem mandatory dimensionality reduction estimate Pr[ X Z = z] for each value of z Attack phase (N attack traces, e.g. with known plaintexts p i ) Log-likelihood score for each key hypothesis k d k = N log Pr[ X = x i Z = f (p i, k)] i=1 April 2018, CISCO E. Prouff 9/22
27 Side Channel Attacks with a Classifier Notations X side channel trace Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X Pr[Z X] Template Attacks Machine Learning Side Channel Attacks Profiling phase (using profiling traces under known Z) manage de-synchronization problem mandatory dimensionality reduction estimate Pr[ X Z = z] for each value of z Attack phase (N attack traces, e.g. with known plaintexts p i ) Log-likelihood score for each key hypothesis k d k = N log Pr[ X = x i Z = f (p i, k)] i=1 April 2018, CISCO E. Prouff 9/22
28 Side Channel Attacks with a Classifier Notations X side channel trace Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X Pr[Z X] Template Attacks Machine Learning Side Channel Attacks Training phase (using training traces under known Z) manage de-synchronization problem mandatory dimensionality reduction estimate Pr[ X Z = z] for each value of z Attack phase (N attack traces, e.g. with known plaintexts p i ) Log-likelihood score for each key hypothesis k d k = N log Pr[ X = x i Z = f (p i, k)] i=1 April 2018, CISCO E. Prouff 9/22
29 Side Channel Attacks with a Classifier Notations X side channel trace Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X Pr[Z X] Template Attacks Machine Learning Side Channel Attacks Training phase (using training traces under known Z) manage de-synchronization problem mandatory dimensionality reduction construct a classifier F ( x) = y Pr[Z X = x] Attack phase (N attack traces, e.g. with known plaintexts p i ) Log-likelihood score for each key hypothesis k d k = N log Pr[ X = x i Z = f (p i, k)] i=1 April 2018, CISCO E. Prouff 9/22
30 Side Channel Attacks with a Classifier Notations X side channel trace Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X Pr[Z X] Template Attacks Machine Learning Side Channel Attacks Training phase (using training traces under known Z) manage de-synchronization problem mandatory dimensionality reduction construct a classifier F ( x) = y Pr[Z X = x] Attack phase (N attack traces, e.g. with known plaintexts p i ) Log-likelihood score for each key hypothesis k d k = N log F ( x i )[f (p i, k)] i=1 April 2018, CISCO E. Prouff 9/22
31 Side Channel Attacks with a Classifier Notations X side channel trace Z target (a cryptographic sensitive variable Z = f (P, K)) Goal: make inference over Z, observing X Pr[Z X] Template Attacks Machine Learning Side Channel Attacks Training phase (using training traces under known Z) } manage de-synchronization problem mandatory dimensionality reduction Integrated approach construct a classifier F ( x) = y Pr[Z X = x] Attack phase (N attack traces, e.g. with known plaintexts p i ) Log-likelihood score for each key hypothesis k d k = N log F ( x i )[f (p i, k)] i=1 April 2018, CISCO E. Prouff 9/22
32 Classification Classification problem Assign to a datum X (e.g. an image) a label Z among a set of possible labels Z = {Cat, Dog, Horse} Classifier April 2018, CISCO E. Prouff 10/22
33 Classification Classification problem Assign to a datum X (e.g. an image) a label Z among a set of possible labels Z = {Cat, Dog, Horse} Classifier Classification 0% 20% 40% 60% Horse Dog Cat April 2018, CISCO E. Prouff 10/22
34 Classification Classification problem Assign to a datum X (e.g. an image) a label Z among a set of possible labels Z = {Cat, Dog, Horse} Classifier Classification 0% 20% 40% 60% Horse Dog Cat Pr[Z X] April 2018, CISCO E. Prouff 10/22
35 Classification Classification problem Assign to a datum X (e.g. an image) a label Z among a set of possible labels Z = {Cat, Dog, Horse} Classifier Classification 0% 20% 40% 60% Horse Dog Cat Pr[Z X] SCA as a Classification Problem x Classifier P(Z X=x) 0% 50% 100% Z=1 Z=0 April 2018, CISCO E. Prouff 10/22
36 Machine Learning Approach Overview of Machine Learning Methodology Human effort: choose a class of algorithms choose a model to fit + tune hyper-parameters Automatic training: automatic tuning of trainable parameters to fit data aaa April 2018, CISCO E. Prouff 11/22
37 Machine Learning Approach Overview of Machine Learning Methodology Human effort: choose a class of algorithms Neural Networks choose a model to fit + tune hyper-parameters Automatic training: automatic tuning of trainable parameters to fit data Stochastic Gradient Descent aaa April 2018, CISCO E. Prouff 11/22
38 Machine Learning Approach Overview of Machine Learning Methodology Human effort: choose a class of algorithms Neural Networks choose a model to fit + tune hyper-parameters MLP, ConvNet Automatic training: automatic tuning of trainable parameters to fit data Stochastic Gradient Descent aaa April 2018, CISCO E. Prouff 11/22
39 Machine Learning Approach Overview of Machine Learning Methodology Human effort: choose a class of algorithms Neural Networks choose a model to fit + tune hyper-parameters MLP, ConvNet Automatic training: automatic tuning of trainable parameters to fit data Stochastic Gradient Descent aaa April 2018, CISCO E. Prouff 11/22
40 Convolutional Neural Networks An answer to translation-invariance Classifier Classification 0% 20% 40% 60% Horse Dog Cat April 2018, CISCO E. Prouff 12/22
41 Convolutional Neural Networks An answer to translation-invariance Classifier Classification 0% 20% 40% 60% Horse Dog Cat April 2018, CISCO E. Prouff 12/22
42 Convolutional Neural Networks An answer to translation-invariance Classifier Classification 0% 20% 40% 60% Horse Dog Cat April 2018, CISCO E. Prouff 12/22
43 Convolutional Neural Networks An answer to translation-invariance Classifier Classification 0% 20% 40% 60% Horse Dog Cat It is important to explicit the data translation-invariance April 2018, CISCO E. Prouff 12/22
44 Convolutional Neural Networks An answer to translation-invariance?? Classification? Classifier 0% 10% 20% 30% 40% 50% Horse Dog Cat It is important to explicit the data translation-invariance April 2018, CISCO E. Prouff 12/22
45 Convolutional Neural Networks An answer to translation-invariance x Classifier P(Z X=x) 0% 50% 100% Z=1 Z=0 It is important to explicit the data translation-invariance April 2018, CISCO E. Prouff 12/22
46 Convolutional Neural Networks An answer to translation-invariance x Classifier P(Z X=x) 0% 50% 100% Z=1 Z=0 It is important to explicit the data translation-invariance April 2018, CISCO E. Prouff 12/22
47 Convolutional Neural Networks An answer to translation-invariance x Classifier P(Z X=x) 0% 50% 100% Z=1 Z=0 It is important to explicit the data translation-invariance April 2018, CISCO E. Prouff 12/22
48 Convolutional Neural Networks An answer to translation-invariance x Classifier P(Z X=x) 0% 50% 100% Z=1 Z=0 It is important to explicit the data translation-invariance Convolutional Neural Networks: share weights across space April 2018, CISCO E. Prouff 12/22
49 Training of Neural Networks Trading Side-Channel Expertise for Deep Learning Expertise... or huge computational power! Training Aims at finding the parameters of the model (aka approximation) for the the statistical dependency between the target value and the leakage. The approximation is done by solving a minimization problem with respect to some metric (aka cost function) The training algorithm has itself some training hyper-parameters: the number of iterations (aka epochs) of the minimization procedure, the number of input traces (aka batch) treated during a single iteration. The architecture of the trained model has architecture hyper-parameters: the size of the layers, the nature of the layers, the number of layers, etc. Finding sound hyper-parameters is the main issue in Deep Learning: this can be done thanks to a good understanding of the underlying structure of the data and/or access to important computational power. April 2018, CISCO E. Prouff 13/22
50 ConvNet typical architecture 2nd Block of CONV layers n conv = 3 and n filters,2 = 8 3rd Block of CONV layers n conv = 3 and n filters,3 = 16 input trace l = 700 1st Block of CONV layers n conv = 3 and n filters,1 = 4 Max POOL (w = 2) Max POOL (w = 2) Block of FC layers + SOFTMAX n dense = 3 April 2018, CISCO E. Prouff 14/22
51 Contents 1. Context and Motivation 1.1 Illustration 1.2 Template Attacks 1.3 Countermeasures 2. A machine learning approach to classification 2.1 Introduction 2.2 The Underlying Classification Problem 2.3 Convolutional Neural Networks 2.4 Training of Models 3. Building a Community Around The Subject 3.1 ASCAD Open Data-Base 3.2 Leakage Characterization and Training 3.3 Results 4. Conclusions April 2018, CISCO E. Prouff 15/22
52 Creation of an open database for Training and Testing ANSSI recently publishes source codes of secure cryptographic implementations for public 8-bit architectures ( data-bases of electromagnetic leakages ( example scripts for the training and testing of Deep Learning models in the context of security evaluations Goal Enable easy fair and easy benchmarking Initiate discussions and exchanges on the application of DL to SCA Create a community of contributors on this subject April 2018, CISCO E. Prouff 16/22
53 Nature of the Observations/Traces Side-channel observations in ASCAD correspond to the masked processing of a simple cryptographic primitive Information leakage validated thanks to SNR characterization snr2 snr Time Samples April 2018, CISCO E. Prouff 17/22
54 Nature of the Observations/Traces Side-channel observations in ASCAD correspond to the masked processing of a simple cryptographic primitive Information leakage validated thanks to SNR characterization snr2 snr Time Samples Validate that shares are manipulated at different times April 2018, CISCO E. Prouff 17/22
55 Nature of the Observations/Traces Side-channel observations in ASCAD correspond to the masked processing of a simple cryptographic primitive Information leakage validated thanks to SNR characterization snr2 snr Time Samples Validate that shares are manipulated at different times Scripts are also proposed to add artificial signal jittering April 2018, CISCO E. Prouff 17/22
56 Our Training Strategy Find a base model architecture and find training hyper-parameters for which a convergence towards the good key hypothesis is visible Fine-tune all the hyper-parameters one after another to get the best efficiency/effectiveness trade-off Table: Benchmarks Summary Parameter Reference Metric Range Choice Training Parameters Epochs - rank vs time 10, 25, 50, 60,..., 100, 150 up to 100 Batch Size - rank vs time 50, 100, Architecture Parameters Blocks n blocks rank, accuracy [2..5] 5 CONV layers n conv rank, accuracy [0..3] 1 Filters n filters,1 rank vs time {2 i ; i [4..7]} 64 Kernel Size - rank {3, 6, 11} 11 FC Layers n dense rank, accuracy vs time [0..3] 2 ACT Function α rank ReLU, Sigmoid, Tanh ReLU Pooling Layer - rank Max, Average, Stride Average Padding - rank Same, Valid Same April 2018, CISCO E. Prouff 18/22
57 The Base Architecture Mean rank of the good-key hypothesis obtained with VGG-16, ResNet-50 and Inception-v3 w.r.t. different epochs: April 2018, CISCO E. Prouff 19/22
58 Comparisons with State-Of-the-Art Methods April 2018, CISCO E. Prouff 20/22
59 Contents 1. Context and Motivation 1.1 Illustration 1.2 Template Attacks 1.3 Countermeasures 2. A machine learning approach to classification 2.1 Introduction 2.2 The Underlying Classification Problem 2.3 Convolutional Neural Networks 2.4 Training of Models 3. Building a Community Around The Subject 3.1 ASCAD Open Data-Base 3.2 Leakage Characterization and Training 3.3 Results 4. Conclusions April 2018, CISCO E. Prouff 21/22
60 Conclusions State-of-the-Art Template Attack separates resynchronization/dimensionality reduction from characterization April 2018, CISCO E. Prouff 22/22
61 Conclusions State-of-the-Art Template Attack separates resynchronization/dimensionality reduction from characterization Deep Learning provides an integrated approach to directly extract information from rough data (no preprocessing) April 2018, CISCO E. Prouff 22/22
62 Conclusions State-of-the-Art Template Attack separates resynchronization/dimensionality reduction from characterization Deep Learning provides an integrated approach to directly extract information from rough data (no preprocessing) Many recent results validate the practical interest of the Machine Learning approach April 2018, CISCO E. Prouff 22/22
63 Conclusions State-of-the-Art Template Attack separates resynchronization/dimensionality reduction from characterization Deep Learning provides an integrated approach to directly extract information from rough data (no preprocessing) Many recent results validate the practical interest of the Machine Learning approach We are in the very beginning and we are still discovering how much Deep Learning is efficient April 2018, CISCO E. Prouff 22/22
64 Conclusions State-of-the-Art Template Attack separates resynchronization/dimensionality reduction from characterization Deep Learning provides an integrated approach to directly extract information from rough data (no preprocessing) Many recent results validate the practical interest of the Machine Learning approach We are in the very beginning and we are still discovering how much Deep Learning is efficient New needs: big data-bases for the training, platforms to enable comparisons and benchmarking, create an open community ML for Embedded Security Analysis, encourage exchanges with the Machine Learning community, understand the efficiency of the current countermeasures April 2018, CISCO E. Prouff 22/22
65 Conclusions State-of-the-Art Template Attack separates resynchronization/dimensionality reduction from characterization Deep Learning provides an integrated approach to directly extract information from rough data (no preprocessing) Many recent results validate the practical interest of the Machine Learning approach We are in the very beginning and we are still discovering how much Deep Learning is efficient New needs: big data-bases for the training, platforms to enable comparisons and benchmarking, create an open community ML for Embedded Security Analysis, encourage exchanges with the Machine Learning community, understand the efficiency of the current countermeasures Thank You! Questions? April 2018, CISCO E. Prouff 22/22
Non-Profiled Deep Learning-Based Side-Channel Attacks
Non-Profiled Deep Learning-Based Side-Channel Attacks Benjamin Timon UL Transaction Security, Singapore benjamin.timon@ul.com Abstract. Deep Learning has recently been introduced as a new alternative to
More informationLowering the Bar: Deep Learning for Side Channel Analysis. Guilherme Perin, Baris Ege, Jasper van December 4, 2018
Lowering the Bar: Deep Learning for Side Channel Analysis Guilherme Perin, Baris Ege, Jasper van Woudenberg @jzvw December 4, 2018 1 Before Signal processing Leakage modeling 2 After 3 Helping security
More informationLecture 37: ConvNets (Cont d) and Training
Lecture 37: ConvNets (Cont d) and Training CS 4670/5670 Sean Bell [http://bbabenko.tumblr.com/post/83319141207/convolutional-learnings-things-i-learned-by] (Unrelated) Dog vs Food [Karen Zack, @teenybiscuit]
More informationPerceptron: This is convolution!
Perceptron: This is convolution! v v v Shared weights v Filter = local perceptron. Also called kernel. By pooling responses at different locations, we gain robustness to the exact spatial location of image
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 Learning and Its Applications
Convolutional Neural Network and Its Application in Image Recognition Oct 28, 2016 Outline 1 A Motivating Example 2 The Convolutional Neural Network (CNN) Model 3 Training the CNN Model 4 Issues and Recent
More informationCMU Lecture 18: Deep learning and Vision: Convolutional neural networks. Teacher: Gianni A. Di Caro
CMU 15-781 Lecture 18: Deep learning and Vision: Convolutional neural networks Teacher: Gianni A. Di Caro DEEP, SHALLOW, CONNECTED, SPARSE? Fully connected multi-layer feed-forward perceptrons: More powerful
More informationDeep Learning Cook Book
Deep Learning Cook Book Robert Haschke (CITEC) Overview Input Representation Output Layer + Cost Function Hidden Layer Units Initialization Regularization Input representation Choose an input representation
More informationINF 5860 Machine learning for image classification. Lecture 11: Visualization Anne Solberg April 4, 2018
INF 5860 Machine learning for image classification Lecture 11: Visualization Anne Solberg April 4, 2018 Reading material The lecture is based on papers: Deep Dream: https://research.googleblog.com/2015/06/inceptionism-goingdeeper-into-neural.html
More informationGenerative Modeling with Convolutional Neural Networks. Denis Dus Data Scientist at InData Labs
Generative Modeling with Convolutional Neural Networks Denis Dus Data Scientist at InData Labs What we will discuss 1. 2. 3. 4. Discriminative vs Generative modeling Convolutional Neural Networks How to
More informationINTRODUCTION TO DEEP LEARNING
INTRODUCTION TO DEEP LEARNING CONTENTS Introduction to deep learning Contents 1. Examples 2. Machine learning 3. Neural networks 4. Deep learning 5. Convolutional neural networks 6. Conclusion 7. Additional
More informationCENG 783. Special topics in. Deep Learning. AlchemyAPI. Week 11. Sinan Kalkan
CENG 783 Special topics in Deep Learning AlchemyAPI Week 11 Sinan Kalkan TRAINING A CNN Fig: http://www.robots.ox.ac.uk/~vgg/practicals/cnn/ Feed-forward pass Note that this is written in terms of the
More informationMachine Learning 13. week
Machine Learning 13. week Deep Learning Convolutional Neural Network Recurrent Neural Network 1 Why Deep Learning is so Popular? 1. Increase in the amount of data Thanks to the Internet, huge amount of
More informationSafety verification for deep neural networks
Safety verification for deep neural networks Marta Kwiatkowska Department of Computer Science, University of Oxford UC Berkeley, 8 th November 2016 Setting the scene Deep neural networks have achieved
More informationEncoder-Decoder Networks for Semantic Segmentation. Sachin Mehta
Encoder-Decoder Networks for Semantic Segmentation Sachin Mehta Outline > Overview of Semantic Segmentation > Encoder-Decoder Networks > Results What is Semantic Segmentation? Input: RGB Image Output:
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 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 informationNeural Networks. CE-725: Statistical Pattern Recognition Sharif University of Technology Spring Soleymani
Neural Networks CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani Outline Biological and artificial neural networks Feed-forward neural networks Single layer
More informationDEEP LEARNING REVIEW. Yann LeCun, Yoshua Bengio & Geoffrey Hinton Nature Presented by Divya Chitimalla
DEEP LEARNING REVIEW Yann LeCun, Yoshua Bengio & Geoffrey Hinton Nature 2015 -Presented by Divya Chitimalla What is deep learning Deep learning allows computational models that are composed of multiple
More informationConvolutional Neural Networks and Supervised Learning
Convolutional Neural Networks and Supervised Learning Eilif Solberg August 30, 2018 Outline Convolutional Architectures Convolutional neural networks Training Loss Optimization Regularization Hyperparameter
More informationPOINT CLOUD DEEP LEARNING
POINT CLOUD DEEP LEARNING Innfarn Yoo, 3/29/28 / 57 Introduction AGENDA Previous Work Method Result Conclusion 2 / 57 INTRODUCTION 3 / 57 2D OBJECT CLASSIFICATION Deep Learning for 2D Object Classification
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 informationComo funciona o Deep Learning
Como funciona o Deep Learning Moacir Ponti (com ajuda de Gabriel Paranhos da Costa) ICMC, Universidade de São Paulo Contact: www.icmc.usp.br/~moacir moacir@icmc.usp.br Uberlandia-MG/Brazil October, 2017
More informationCSE 559A: Computer Vision
CSE 559A: Computer Vision Fall 2018: T-R: 11:30-1pm @ Lopata 101 Instructor: Ayan Chakrabarti (ayan@wustl.edu). Course Staff: Zhihao Xia, Charlie Wu, Han Liu http://www.cse.wustl.edu/~ayan/courses/cse559a/
More informationStructured Prediction using Convolutional Neural Networks
Overview Structured Prediction using Convolutional Neural Networks Bohyung Han bhhan@postech.ac.kr Computer Vision Lab. Convolutional Neural Networks (CNNs) Structured predictions for low level computer
More informationCPSC 340: Machine Learning and Data Mining. Principal Component Analysis Fall 2016
CPSC 340: Machine Learning and Data Mining Principal Component Analysis Fall 2016 A2/Midterm: Admin Grades/solutions will be posted after class. Assignment 4: Posted, due November 14. Extra office hours:
More informationDeep Learning For Video Classification. Presented by Natalie Carlebach & Gil Sharon
Deep Learning For Video Classification Presented by Natalie Carlebach & Gil Sharon Overview Of Presentation Motivation Challenges of video classification Common datasets 4 different methods presented in
More informationFuzzy Set Theory in Computer Vision: Example 3, Part II
Fuzzy Set Theory in Computer Vision: Example 3, Part II Derek T. Anderson and James M. Keller FUZZ-IEEE, July 2017 Overview Resource; CS231n: Convolutional Neural Networks for Visual Recognition https://github.com/tuanavu/stanford-
More informationAccelerating Convolutional Neural Nets. Yunming Zhang
Accelerating Convolutional Neural Nets Yunming Zhang Focus Convolutional Neural Nets is the state of the art in classifying the images The models take days to train Difficult for the programmers to tune
More informationStudy of Residual Networks for Image Recognition
Study of Residual Networks for Image Recognition Mohammad Sadegh Ebrahimi Stanford University sadegh@stanford.edu Hossein Karkeh Abadi Stanford University hosseink@stanford.edu Abstract Deep neural networks
More informationPractical Electromagnetic Template Attack on HMAC
Practical Electromagnetic Template Attack on HMAC Pierre Alain Fouque 1 Gaétan Leurent 1 Denis Réal 2,3 Frédéric Valette 2 1ENS,75Paris,France. 2CELAR,35Bruz,France. 3INSA-IETR,35Rennes,France. September
More informationNVIDIA FOR DEEP LEARNING. Bill Veenhuis
NVIDIA FOR DEEP LEARNING Bill Veenhuis bveenhuis@nvidia.com Nvidia is the world s leading ai platform ONE ARCHITECTURE CUDA 2 GPU: Perfect Companion for Accelerating Apps & A.I. CPU GPU 3 Intro to AI AGENDA
More informationEE-559 Deep learning Networks for semantic segmentation
EE-559 Deep learning 7.4. Networks for semantic segmentation François Fleuret https://fleuret.org/ee559/ Mon Feb 8 3:35:5 UTC 209 ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE The historical approach to image
More informationAkarsh Pokkunuru EECS Department Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
Akarsh Pokkunuru EECS Department 03-16-2017 Contractive Auto-Encoders: Explicit Invariance During Feature Extraction 1 AGENDA Introduction to Auto-encoders Types of Auto-encoders Analysis of different
More informationFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren Kaiming He Ross Girshick Jian Sun Present by: Yixin Yang Mingdong Wang 1 Object Detection 2 1 Applications Basic
More informationIndex. Springer Nature Switzerland AG 2019 B. Moons et al., Embedded Deep Learning,
Index A Algorithmic noise tolerance (ANT), 93 94 Application specific instruction set processors (ASIPs), 115 116 Approximate computing application level, 95 circuits-levels, 93 94 DAS and DVAS, 107 110
More informationMachine Learning. Deep Learning. Eric Xing (and Pengtao Xie) , Fall Lecture 8, October 6, Eric CMU,
Machine Learning 10-701, Fall 2015 Deep Learning Eric Xing (and Pengtao Xie) Lecture 8, October 6, 2015 Eric Xing @ CMU, 2015 1 A perennial challenge in computer vision: feature engineering SIFT Spin image
More informationThe exam is closed book, closed notes except your one-page (two-sided) cheat sheet.
CS 189 Spring 2015 Introduction to Machine Learning Final You have 2 hours 50 minutes for the exam. The exam is closed book, closed notes except your one-page (two-sided) cheat sheet. No calculators or
More informationLSTM for Language Translation and Image Captioning. Tel Aviv University Deep Learning Seminar Oran Gafni & Noa Yedidia
1 LSTM for Language Translation and Image Captioning Tel Aviv University Deep Learning Seminar Oran Gafni & Noa Yedidia 2 Part I LSTM for Language Translation Motivation Background (RNNs, LSTMs) Model
More informationKnow your data - many types of networks
Architectures Know your data - many types of networks Fixed length representation Variable length representation Online video sequences, or samples of different sizes Images Specific architectures for
More informationInception and Residual Networks. Hantao Zhang. Deep Learning with Python.
Inception and Residual Networks Hantao Zhang Deep Learning with Python https://en.wikipedia.org/wiki/residual_neural_network Deep Neural Network Progress from Large Scale Visual Recognition Challenge (ILSVRC)
More informationMask R-CNN. By Kaiming He, Georgia Gkioxari, Piotr Dollar and Ross Girshick Presented By Aditya Sanghi
Mask R-CNN By Kaiming He, Georgia Gkioxari, Piotr Dollar and Ross Girshick Presented By Aditya Sanghi Types of Computer Vision Tasks http://cs231n.stanford.edu/ Semantic vs Instance Segmentation Image
More informationQuo Vadis, Action Recognition? A New Model and the Kinetics Dataset. By Joa õ Carreira and Andrew Zisserman Presenter: Zhisheng Huang 03/02/2018
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset By Joa õ Carreira and Andrew Zisserman Presenter: Zhisheng Huang 03/02/2018 Outline: Introduction Action classification architectures
More informationIntroduction to Data Science. Introduction to Data Science with Python. Python Basics: Basic Syntax, Data Structures. Python Concepts (Core)
Introduction to Data Science What is Analytics and Data Science? Overview of Data Science and Analytics Why Analytics is is becoming popular now? Application of Analytics in business Analytics Vs Data
More informationDeep Learning. Vladimir Golkov Technical University of Munich Computer Vision Group
Deep Learning Vladimir Golkov Technical University of Munich Computer Vision Group 1D Input, 1D Output target input 2 2D Input, 1D Output: Data Distribution Complexity Imagine many dimensions (data occupies
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 informationSpread: a new layer for profiled deep-learning side-channel attacks
Spread: a new layer for profiled deep-learning side-channel attacks Christophe Pfeifer 1,2 and Patrick Haddad 3 1 Karlsruhe Institute of Technology, Karlsruhe, Germany 2 Grenoble INP - Ensimag, Grenoble,
More informationDissecting Leakage Resilient PRFs with Multivariate Localized EM Attacks
Dissecting Leakage Resilient PRFs with Multivariate Localized EM Attacks A Practical Security Evaluation on FPGA Florian Unterstein Johann Heyszl Fabrizio De Santis a Robert Specht, 13.04.2017 a Technical
More informationMoonRiver: Deep Neural Network in C++
MoonRiver: Deep Neural Network in C++ Chung-Yi Weng Computer Science & Engineering University of Washington chungyi@cs.washington.edu Abstract Artificial intelligence resurges with its dramatic improvement
More informationKeras: Handwritten Digit Recognition using MNIST Dataset
Keras: Handwritten Digit Recognition using MNIST Dataset IIT PATNA February 9, 2017 1 / 24 OUTLINE 1 Introduction Keras: Deep Learning library for Theano and TensorFlow 2 Installing Keras Installation
More information6. Convolutional Neural Networks
6. Convolutional Neural Networks CS 519 Deep Learning, Winter 2017 Fuxin Li With materials from Zsolt Kira Quiz coming up Next Thursday (2/2) 20 minutes Topics: Optimization Basic neural networks No Convolutional
More informationDECISION TREES & RANDOM FORESTS X CONVOLUTIONAL NEURAL NETWORKS
DECISION TREES & RANDOM FORESTS X CONVOLUTIONAL NEURAL NETWORKS Deep Neural Decision Forests Microsoft Research Cambridge UK, ICCV 2015 Decision Forests, Convolutional Networks and the Models in-between
More informationFuzzy Set Theory in Computer Vision: Example 3
Fuzzy Set Theory in Computer Vision: Example 3 Derek T. Anderson and James M. Keller FUZZ-IEEE, July 2017 Overview Purpose of these slides are to make you aware of a few of the different CNN architectures
More informationBig Data Era Time Domain Astronomy
Big Data Era Time Domain Astronomy Pablo A. Estevez Joint work with Guillermo Cabrera-Vives, Ignacio Reyes, Francisco Förster, Juan-Carlos Maureira and Pablo Huentelemu Millennium Institute of Astrophysics,
More informationCNN Basics. Chongruo Wu
CNN Basics Chongruo Wu Overview 1. 2. 3. Forward: compute the output of each layer Back propagation: compute gradient Updating: update the parameters with computed gradient Agenda 1. Forward Conv, Fully
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 informationCNNS FROM THE BASICS TO RECENT ADVANCES. Dmytro Mishkin Center for Machine Perception Czech Technical University in Prague
CNNS FROM THE BASICS TO RECENT ADVANCES Dmytro Mishkin Center for Machine Perception Czech Technical University in Prague ducha.aiki@gmail.com OUTLINE Short review of the CNN design Architecture progress
More informationDeep Learning for Computer Vision with MATLAB By Jon Cherrie
Deep Learning for Computer Vision with MATLAB By Jon Cherrie 2015 The MathWorks, Inc. 1 Deep learning is getting a lot of attention "Dahl and his colleagues won $22,000 with a deeplearning system. 'We
More informationCIS581: Computer Vision and Computational Photography Project 4, Part B: Convolutional Neural Networks (CNNs) Due: Dec.11, 2017 at 11:59 pm
CIS581: Computer Vision and Computational Photography Project 4, Part B: Convolutional Neural Networks (CNNs) Due: Dec.11, 2017 at 11:59 pm Instructions CNNs is a team project. The maximum size of a team
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 informationRECENT years have witnessed the rapid growth of image. SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval
SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval Jian Zhang, Yuxin Peng, and Junchao Zhang arxiv:607.08477v [cs.cv] 28 Jul 206 Abstract The hashing methods have been widely used for efficient
More informationDeep Face Recognition. Nathan Sun
Deep Face Recognition Nathan Sun Why Facial Recognition? Picture ID or video tracking Higher Security for Facial Recognition Software Immensely useful to police in tracking suspects Your face will be an
More informationConvolutional Neural Networks: Applications and a short timeline. 7th Deep Learning Meetup Kornel Kis Vienna,
Convolutional Neural Networks: Applications and a short timeline 7th Deep Learning Meetup Kornel Kis Vienna, 1.12.2016. Introduction Currently a master student Master thesis at BME SmartLab Started deep
More informationDeep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations, and Hardware Implications
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations, and Hardware Implications Jongsoo Park Facebook AI System SW/HW Co-design Team Sep-21 2018 Team Introduction
More informationDefense Data Generation in Distributed Deep Learning System Se-Yoon Oh / ADD-IDAR
Defense Data Generation in Distributed Deep Learning System Se-Yoon Oh / 2017. 10. 31 syoh@add.re.kr Page 1/36 Overview 1. Introduction 2. Data Generation Synthesis 3. Distributed Deep Learning 4. Conclusions
More informationMulti-Glance Attention Models For Image Classification
Multi-Glance Attention Models For Image Classification Chinmay Duvedi Stanford University Stanford, CA cduvedi@stanford.edu Pararth Shah Stanford University Stanford, CA pararth@stanford.edu Abstract We
More informationConvolutional Neural Networks
NPFL114, Lecture 4 Convolutional Neural Networks Milan Straka March 25, 2019 Charles University in Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics unless otherwise
More informationInception Network Overview. David White CS793
Inception Network Overview David White CS793 So, Leonardo DiCaprio dreams about dreaming... https://m.media-amazon.com/images/m/mv5bmjaxmzy3njcxnf5bml5banbnxkftztcwnti5otm0mw@@._v1_sy1000_cr0,0,675,1 000_AL_.jpg
More informationCOMP9444 Neural Networks and Deep Learning 7. Image Processing. COMP9444 c Alan Blair, 2017
COMP9444 Neural Networks and Deep Learning 7. Image Processing COMP9444 17s2 Image Processing 1 Outline Image Datasets and Tasks Convolution in Detail AlexNet Weight Initialization Batch Normalization
More informationLecture 20: Neural Networks for NLP. Zubin Pahuja
Lecture 20: Neural Networks for NLP Zubin Pahuja zpahuja2@illinois.edu courses.engr.illinois.edu/cs447 CS447: Natural Language Processing 1 Today s Lecture Feed-forward neural networks as classifiers simple
More informationFUSION MODEL BASED ON CONVOLUTIONAL NEURAL NETWORKS WITH TWO FEATURES FOR ACOUSTIC SCENE CLASSIFICATION
Please contact the conference organizers at dcasechallenge@gmail.com if you require an accessible file, as the files provided by ConfTool Pro to reviewers are filtered to remove author information, and
More informationOn the Practical Security of a Leakage Resilient Masking Scheme
On the Practical Security of a Leakage Resilient Masking Scheme T. Roche thomas.roche@ssi.gouv.fr Joint work with E. Prouff and M. Rivain CT-RSA 2014 Feb. 2014 Side Channel Analysis Side Channel Attacks
More informationPredict the Likelihood of Responding to Direct Mail Campaign in Consumer Lending Industry
Predict the Likelihood of Responding to Direct Mail Campaign in Consumer Lending Industry Jincheng Cao, SCPD Jincheng@stanford.edu 1. INTRODUCTION When running a direct mail campaign, it s common practice
More informationIntro to Deep Learning. Slides Credit: Andrej Karapathy, Derek Hoiem, Marc Aurelio, Yann LeCunn
Intro to Deep Learning Slides Credit: Andrej Karapathy, Derek Hoiem, Marc Aurelio, Yann LeCunn Why this class? Deep Features Have been able to harness the big data in the most efficient and effective
More informationChallenges motivating deep learning. Sargur N. Srihari
Challenges motivating deep learning Sargur N. srihari@cedar.buffalo.edu 1 Topics In Machine Learning Basics 1. Learning Algorithms 2. Capacity, Overfitting and Underfitting 3. Hyperparameters and Validation
More informationConstrained Convolutional Neural Networks for Weakly Supervised Segmentation. Deepak Pathak, Philipp Krähenbühl and Trevor Darrell
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation Deepak Pathak, Philipp Krähenbühl and Trevor Darrell 1 Multi-class Image Segmentation Assign a class label to each pixel in
More informationCS 1674: Intro to Computer Vision. Neural Networks. Prof. Adriana Kovashka University of Pittsburgh November 16, 2016
CS 1674: Intro to Computer Vision Neural Networks Prof. Adriana Kovashka University of Pittsburgh November 16, 2016 Announcements Please watch the videos I sent you, if you haven t yet (that s your reading)
More informationMulti-Task Self-Supervised Visual Learning
Multi-Task Self-Supervised Visual Learning Sikai Zhong March 4, 2018 COMPUTER SCIENCE Table of contents 1. Introduction 2. Self-supervised Tasks 3. Architectures 4. Experiments 1 Introduction Self-supervised
More informationIntroduction to Neural Networks
Introduction to Neural Networks Jakob Verbeek 2017-2018 Biological motivation Neuron is basic computational unit of the brain about 10^11 neurons in human brain Simplified neuron model as linear threshold
More informationTwo-Stream Convolutional Networks for Action Recognition in Videos
Two-Stream Convolutional Networks for Action Recognition in Videos Karen Simonyan Andrew Zisserman Cemil Zalluhoğlu Introduction Aim Extend deep Convolution Networks to action recognition in video. Motivation
More informationCPSC 340: Machine Learning and Data Mining. Deep Learning Fall 2018
CPSC 340: Machine Learning and Data Mining Deep Learning Fall 2018 Last Time: Multi-Dimensional Scaling Multi-dimensional scaling (MDS): Non-parametric visualization: directly optimize the z i locations.
More informationMartian lava field, NASA, Wikipedia
Martian lava field, NASA, Wikipedia Old Man of the Mountain, Franconia, New Hampshire Pareidolia http://smrt.ccel.ca/203/2/6/pareidolia/ Reddit for more : ) https://www.reddit.com/r/pareidolia/top/ Pareidolia
More informationMidterm Review. CS230 Fall 2018
Midterm Review CS23 Fall 28 Broadcasting Calculating Means How would you calculate the means across the rows of the following matrix? How about the columns? Calculating Means How would you calculate the
More informationCS231N Section. Video Understanding 6/1/2018
CS231N Section Video Understanding 6/1/2018 Outline Background / Motivation / History Video Datasets Models Pre-deep learning CNN + RNN 3D convolution Two-stream What we ve seen in class so far... Image
More informationSpatial Localization and Detection. Lecture 8-1
Lecture 8: Spatial Localization and Detection Lecture 8-1 Administrative - Project Proposals were due on Saturday Homework 2 due Friday 2/5 Homework 1 grades out this week Midterm will be in-class on Wednesday
More informationAn Introduction to NNs using Keras
An Introduction to NNs using Keras Michela Paganini michela.paganini@cern.ch Yale University 1 Keras Modular, powerful and intuitive Deep Learning python library built on Theano and TensorFlow Minimalist,
More informationA Deep Learning Approach to Vehicle Speed Estimation
A Deep Learning Approach to Vehicle Speed Estimation Benjamin Penchas bpenchas@stanford.edu Tobin Bell tbell@stanford.edu Marco Monteiro marcorm@stanford.edu ABSTRACT Given car dashboard video footage,
More informationMachine Learning With Python. Bin Chen Nov. 7, 2017 Research Computing Center
Machine Learning With Python Bin Chen Nov. 7, 2017 Research Computing Center Outline Introduction to Machine Learning (ML) Introduction to Neural Network (NN) Introduction to Deep Learning NN Introduction
More informationHarp-DAAL for High Performance Big Data Computing
Harp-DAAL for High Performance Big Data Computing Large-scale data analytics is revolutionizing many business and scientific domains. Easy-touse scalable parallel techniques are necessary to process big
More informationFully Convolutional Networks for Semantic Segmentation
Fully Convolutional Networks for Semantic Segmentation Jonathan Long* Evan Shelhamer* Trevor Darrell UC Berkeley Chaim Ginzburg for Deep Learning seminar 1 Semantic Segmentation Define a pixel-wise labeling
More informationCAP 6412 Advanced Computer Vision
CAP 6412 Advanced Computer Vision http://www.cs.ucf.edu/~bgong/cap6412.html Boqing Gong April 21st, 2016 Today Administrivia Free parameters in an approach, model, or algorithm? Egocentric videos by Aisha
More informationLecture 13. Deep Belief Networks. Michael Picheny, Bhuvana Ramabhadran, Stanley F. Chen
Lecture 13 Deep Belief Networks Michael Picheny, Bhuvana Ramabhadran, Stanley F. Chen IBM T.J. Watson Research Center Yorktown Heights, New York, USA {picheny,bhuvana,stanchen}@us.ibm.com 12 December 2012
More informationFusion of Mini-Deep Nets
Rochester Institute of Technology RIT Scholar Works Theses Thesis/Dissertation Collections 5-2016 Fusion of Mini-Deep Nets Sai Prasad Nooka spn8235@rit.edu Follow this and additional works at: http://scholarworks.rit.edu/theses
More informationComputer Vision Lecture 16
Computer Vision Lecture 16 Deep Learning for Object Categorization 14.01.2016 Bastian Leibe RWTH Aachen http://www.vision.rwth-aachen.de leibe@vision.rwth-aachen.de Announcements Seminar registration period
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 informationConvolutional Neural Network Layer Reordering for Acceleration
R1-15 SASIMI 2016 Proceedings Convolutional Neural Network Layer Reordering for Acceleration Vijay Daultani Subhajit Chaudhury Kazuhisa Ishizaka System Platform Labs Value Co-creation Center System Platform
More information1 Overview Definitions (read this section carefully) 2
MLPerf User Guide Version 0.5 May 2nd, 2018 1 Overview 2 1.1 Definitions (read this section carefully) 2 2 General rules 3 2.1 Strive to be fair 3 2.2 System and framework must be consistent 4 2.3 System
More informationSemantic Segmentation. Zhongang Qi
Semantic Segmentation Zhongang Qi qiz@oregonstate.edu Semantic Segmentation "Two men riding on a bike in front of a building on the road. And there is a car." Idea: recognizing, understanding what's in
More informationIndex. Umberto Michelucci 2018 U. Michelucci, Applied Deep Learning,
A Acquisition function, 298, 301 Adam optimizer, 175 178 Anaconda navigator conda command, 3 Create button, 5 download and install, 1 installing packages, 8 Jupyter Notebook, 11 13 left navigation pane,
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 information