Greedy Layerwise Learning Can Scale to ImageNet Edouard Oyallon - PowerPoint PPT Presentation

Sep 30, 2022 •193 likes •552 views

1 Centre for Visual Computing Greedy Layerwise Learning Can Scale to ImageNet Edouard Oyallon Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon <latexit

1 Centre for Visual Computing Greedy Layerwise Learning Can Scale to ImageNet Edouard Oyallon Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon

Recommend

CS7015 (Deep Learning) : Lecture 9 Greedy Layerwise Pre-training, Better activation functions,

CS7015 (Deep Learning) : Lecture 9 Greedy Layerwise Pre-training, Better activation functions, Better weight initialization methods, Batch Normalization Mitesh M. Khapra Department of Computer Science and Engineering Indian Institute of

1.19k views • 69 slides

Large scale greedy feature-selection for multi-target learning Antti Airola, Tapio Pahikkala et

Large scale greedy feature-selection for multi-target learning Antti Airola, Tapio Pahikkala et al. ECML 2015 BigTargets Workshop Antti Airola, Tapio Pahikkala et al. Large scale greedy feature-selection for multi-target learning Overview

354 views • 17 slides

Augmentation Introduction ImageNet Classification with Deep Convolutional Neural Networks,

Day 2 Lecture 2 Augmentation Introduction ImageNet Classification with Deep Convolutional Neural Networks, Krizhevsky A., 2012 ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) 1.2 million training images , 50,000 validation images, and

537 views • 14 slides

CNN Architectures ILSVRC: Imagenet Large Scale Visual Recognition Challenge [Russakovsky et al

CS4501: Introduction to Computer Vision CNN Architectures ILSVRC: Imagenet Large Scale Visual Recognition Challenge [Russakovsky et al 2014] The Problem: Classification Classify an image into 1000 possible classes: e.g. Abyssinian cat,

673 views • 36 slides

Imagenet Xavier Gir-i-Nieto ImageNet ILSRVC Li Fei-Fei, How were teaching computers to

Day 2 Lecture 4 Imagenet Xavier Gir-i-Nieto ImageNet ILSRVC Li Fei-Fei, How were teaching computers to understand pictures TEDTalks 2014. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Fei-Fei, L.

855 views • 52 slides

Modern CNNs Prof. Seungchul Lee Industrial AI Lab. ImageNet Human performance = 5.1 % from

Modern CNNs Prof. Seungchul Lee Industrial AI Lab. ImageNet Human performance = 5.1 % from Kaiming He slides "Deep residual learning for image recognition," ICML, 2016. 2 ImageNet 3 LeNet CNN = Convolutional Neural Networks

792 views • 15 slides

Greedy Algorithms Chapter 16 1 CPTR 430 Algorithms Greedy Algorithms Greedy Algorithms For

Greedy Algorithms Chapter 16 1 CPTR 430 Algorithms Greedy Algorithms Greedy Algorithms For some optimization problems, dynamic programming algorithms are overkill A greedy algorithm always makes a choice that looks best at the moment

1.09k views • 65 slides

Training ImageNet in 15 Minutes With ChainerMN: A Scalable Distributed Deep Learning Framework

Training ImageNet in 15 Minutes With ChainerMN: A Scalable Distributed Deep Learning Framework Takuya Akiba, Shuji Suzuki, Keisuke Fukuda, and Kota Uenishi Preferred Networks, Inc. Who are we? Preferred Networks, Inc. (PFN): A Tokyo-based

666 views • 50 slides

CS 170 Section 4 Greedy Algorithms I Owen Jow | owenjow@berkeley.edu Agenda Greedy

CS 170 Section 4 Greedy Algorithms I Owen Jow | owenjow@berkeley.edu Agenda Greedy algorithms Set cover Greedy Algorithms Greedy Algorithms A greedy algorithm repeatedly selects the best-looking option according to some metric,

108 views • 6 slides

ACCELERATED COMPUTING FOR AI Bryan Catanzaro, 28 October 2017 DEEP LEARNING BIG BANG ImageNet

ACCELERATED COMPUTING FOR AI Bryan Catanzaro, 28 October 2017 DEEP LEARNING BIG BANG ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky Ilya Sutskever Geoffrey E. Hinton University of Toronto University of

269 views • 25 slides

Common Architecture Elements SIGGRAPH Asia Course CreativeAI: Deep Learning for Graphics 1

Common Architecture Elements SIGGRAPH Asia Course CreativeAI: Deep Learning for Graphics 1 Classification, Segmentation, Detection ImageNet classification performance (for up-to-date top-performers see leaderboards of datasets like ImageNet

707 views • 48 slides

From greedy approximation to greedy optimization Vladimir Temlyakov July, 2014 Vladimir

Introduction Greedy approximation in Hilbert spaces Greedy approximation in Banach spaces Greedy algorithms for convex optimization Lebesgue-type inequality From greedy approximation to greedy optimization Vladimir Temlyakov July, 2014

1.03k views • 70 slides

Weihrauch-completeness for layerwise computability 1 Arno Pauly Clare College University of

Weihrauch-completeness for layerwise computability 1 Arno Pauly Clare College University of Cambridge CCR 2015, Heidelberg 1 Joint work with George Davie & Willem Fouch (UNISA). Outline Definitions The main result Examples A

374 views • 23 slides

OverFeat Classification, Localization and Detection using Deep Learning Pierre Sermanet, David

OverFeat Classification, Localization and Detection using Deep Learning Pierre Sermanet, David Eigen, Michael Mathieu, Xiang Zhang, Rob Fergus, Yann LeCun New York University ICCV 2013 ImageNet Large Scale Visual Recognition Challenge

656 views • 40 slides

Greedy Algorithms The Greedy strategy is (just like D&C or DP) a design paradigm . General

Greedy Algorithms The Greedy strategy is (just like D&C or DP) a design paradigm . General idea: Greedy algorithms always make choices that look best at the moment. They do not always yield optimal results, but in many cases they do

424 views • 27 slides

Greedy On-Line Planning - abstract overview: what is greedy on-line planning? Part 1: - greedy

Greedy On-Line Planning Greedy On-Line Planning - abstract overview: what is greedy on-line planning? Part 1: - greedy on-line planning makes planning tractable example: greedy localization Sven Koenig Part 2: - greedy on-line planning is

606 views • 36 slides

Chapter 16: Greedy Algorithms Greedy is a strategy that works well on optimization problems with

Chapter 16: Greedy Algorithms Greedy is a strategy that works well on optimization problems with the following characteristics: 1. Greedy-choice property: A global optimum can be arrived at by selecting a local optimum. 2. Optimal substructure:

664 views • 24 slides

Hyperbolicity of the Layerwise Discretized Shallow Water equations The bilayer case Martin

Hyperbolicity of the Layerwise Discretized Shallow Water equations The bilayer case Martin Parisot - Ange Inria Paris-Rocquencourt in collaboration with Nina Aguillon (P6) Emmanuel Audusse (P13) Edwige Godlewski (P6 - Ange ) and EGRIN - 2 juin

302 views • 28 slides

Geirhos et al. (2019) Introduction ImageNet classifjcation with CNNs Which image cues are

IMAGENET-TRAINED CNNS ARE BIASED TOWARDS TEXTURE; INCREASING SHAPE BIAS IMPROVES ACCURACY AND ROBUSTNESS Geirhos et al. (2019) Introduction ImageNet classifjcation with CNNs Which image cues are learned How infmuential they are

221 views • 18 slides

Greedy embedding of a graph Greedy embedding of a graph 99 Greedy embedding Greedy embedding

Given a graph, find an embedding s.t. greedy routing works Greedy embedding of a graph Greedy embedding of a graph 99 Greedy embedding Greedy embedding Given a graph G, find an embedding of the vertices in R d , s.t. for each pair of

361 views • 23 slides

1 Greedy Sequential Covering Example Greedy Sequential Covering Example Y Y + + + + + +

Learning Rules If-then rules in logic are a standard representation of knowledge that have proven useful in expert-systems and other AI systems CS 391L: Machine Learning: In propositional logic a set of rules for a concept is equivalent

396 views • 14 slides

Greedy routing by distributed D l Delaunay triangulation t i l ti 4/4/2017 Greedy Routing (S.

Greedy routing by distributed D l Delaunay triangulation t i l ti 4/4/2017 Greedy Routing (S. S. Lam) 1 Gr Greedy Routing y out ng It is scalable to a large n t network k d (destination) o because each node stores info about its

636 views • 46 slides

ImageNet Classification with Deep Convolutional Neural Networks Krizhevsky et. all Outline

ImageNet Classification with Deep Convolutional Neural Networks Krizhevsky et. all Outline Introduction DataSet Architecture of the Network Reducing overfitting Learning Results Discussion Introduction

1.3k views • 31 slides

Greedy Algorithms Algorithm Theory WS 2013/14 Fabian Kuhn Greedy Algorithms No clear

Chapter 2 Greedy Algorithms Algorithm Theory WS 2013/14 Fabian Kuhn Greedy Algorithms No clear definition, but essentially: In each step make the choice that looks best at the moment! Depending on problem, greedy algorithms can give

409 views • 40 slides