Learning from Infinite Training Examples

Learning from Infinite Training Examples

3.18.2009, 3.19.2009

Prepared for NKU and NUTN seminars

Presenter: Chun-Nan Hsu ( 許鈞南 )Institute of Information ScienceAcademia SinicaTaipei, Taiwan

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The Ever Growing Web(Zhuang, -400)

Human life is finite, but knowledge is

infinite. Following the infinite with the finite is doomed to fail.

人之生也有涯，而知也無涯。以有涯隨無涯，殆矣。莊子，西元前四百年

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Analogously…

Computing power is finite, but the Web is infinite. Mining infinite Web with finite

computing power…

is doomed to fail?

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Other “holy grails” in Artificial Intelligence

Learning to understand natural languages

Learning to recognize millions of objects in computer vision

Speech recognition in noisy environment, such as in a car

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On-Line Learning vs. Off-Line Learning

Nothing to do with human learning by browsing the web

Definition: Given a set of new training data, online learner can update its model without

reading old data while improving its performance.

By contrast, off-line learner must combine old and new data and start the learning all over again, otherwise the performance willsuffer.

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Off-Line Learning

Nearly all popular ML algorithms are off-line today

They scan the training examples many passes iteratively until an objective function is minimized

For example: SMO algorithm for SVM L-BFGS algorithm for CRF EM algorithm for HMM and GMM Etc.

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Why on-line learning?

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Single-pass on-line learning

The key for on-line learning to win is to achieve satisfying performance right after scanning the new training

examples for a single pass only

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Previous work on on-line learning

Perceptron Rosenblatt 1957

Stochastic Gradient Descent Widrow & Hoff 1960

Bregment Divergence Azoury & Warmuth 2001

MIRA (Large Margin) Crammer & Singer 2003

LaRank Borde & Bottou 2005, 2007

EG Collins & Peter Bartlet et al. 2008

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Stochastic Gradient Descent (SGD)

Learning is to minimize a loss function given training examples

0);(

GL

DL

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Optimal Step Size(Benveniste et al. 1990, Murata et al. 1998)

Solving gradient = 0 by Newton’s method

Step size is asymptotically optimal if it approaches to

);( )(1)()1( DLH ttt

1

1)(

t

Ht

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Single-Pass Result (Bottou & LeCun 2004)

Optimum for n+1 examples is a Newton step away from the optimum for n examples

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*11

**1

1);(

1

1

noBLH

n nnnnn

*n

*1n

);( nDL );( 1nDL

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2nd Order SGD

2nd order SGD (2SGD): Adjusting the step size to approach to Hessian

Good News: from previous work, given sufficiently large training examples, 2SGD achieves empirical optimum in a single pass!

Bad News: it is prohibitively expensive to compute H-1

e.g. 10K features, H will be a 10K by 10K matrix = 100M floating point array

How about 1M features?

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Approximating Jacobian(Aitken 1925, Schafer 1997)

Learning algorithms can be considered as fixed-point iteration mapping =M()

Taylor expansion gives

Eigenvalues of J can be approximated by

)()1(

)1()2(

ti

ti

ti

ti

i

)()( *)(*)()1( ttt JM

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Approximating Hessian

Consider SGD mapping as a fixed-point iteration, too.

since J=M’=I-H, we have eig(J)=eig(M’)=eig(I-H), therefore, (since H is symmetric) eig(J)=1- eig(H) eig(H-1)= / 1-eig(J)= / 1-γ.

);()( )()( tt BLM

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Estimating Eigenvalue Periodically

Since the mapping of SGD is stochastic, estimating the eigenvalues at each iteration may yield inaccurate estimations.

To make the mapping more stationary, we use Mb=M(M(…M(θ)…))

From the law of large number, b consecutive mappings, Mb, will be less “stochastic”

From Equation (4), we can estimate eig(Jb) by

)()(

)()2(

ti

bti

bti

bti

i

b

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The PSA algorithm (Huang, Chang & Hsu 2007)

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Experimental Results

Conditional Random Fields (CRF) (Lafferty et al. 2001)

Sequence labeling problems – gene mention tagging

Conditional Random Fields

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In effect, CRF encodes a probabilistic rule-based system with rules of the form:

If fj1(X,Y) & fj2(X,Y) & … & fjn(X,Y) are non-zero,

then the labels of the sequence are Y

with score P(Y|X)

If we have d features and considers w context, then an order-1 CRF encodes this many rules:

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2||||2 yx wd 03/18/2009

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Tasks and Setups

CoNLL 2000 base NP Tag Noun phrases 8936 training 2012 test 3 tags, 1015662 features

CoNLL 2000 chunking Tag 11 POS types 8936 training 2012 test 23 tags, 7448606

features Performance measure: F-

score:

BioNLP/NLPBA 2004 Tag 5 types of bio-

entities (e.g., gene, protein, cell lines, etc.)

18546 training 3856 test

5977675 features BioCreative 2

Tag gene names 15000 training 5000

test 10242972 features

pr

rpF

FPTP

TPp

FNTP

TPr

2,,

Feature types for BioCreative 2

2203/18/2009O(22M ) rules are encoded in our CRF model!!!

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Convergence PerformanceCoNLL 2000 base NP

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Convergence PerformanceCoNLL chunking

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Convergence PerformanceBioNLP/NLPBA 2004

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Convergence PerformanceBioCreative 2

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Execution TimeCoNLL 2000 base NP

First Pass 23.74 sec

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Execution TimeCoNLL chunking

First Pass 196.44 sec

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Execution TimeBioNLP/NLPBA 2004

First Pass 287.48 sec

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Execution TimeBioCreative 2

First Pass 394.04 sec

Experimental results for linear SVM and convolutional neural net

Data sets

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Linear SVM

Convolutional Neural Net (5 layers)

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** Layer trick -- Step sizes in the lower layers should be larger than in the higher layer

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Mini-conclusion: Single-Pass

By approximating Jacobian, we can approximate Hessian, too

By approximating Hessian, we can achieve near-optimal single-pass performance in practice

With a single-pass on-line learner, virtually infinitely many training examples can be used

PSA is a member in the family of “discretized Newton Methods”

Other well-known members include Secant method (aka. Quickprop) Steffensen’s method (aka. Triple Jump)

General form of these methods

where A is a matrix designed to

approximate the hessian matrix without actually computing the derivative

)(],[ )(1)()1( ttt ghA

Analysis of PSA

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PSA

PSA is not secant nor is it steffensen’s method

PSA iterates a 2b-step “parallel chord” method (i.e., fixed rate SGD) followed by an approximated Newton step Off-line 2-step parallel chord method is

known to have an order 4 convergence

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Convergence analysis of PSA

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Are we there yet?

With single-pass on-line learning, we can learn from infinite training examples now, at least in theory

A cheaper, quicker method to annotate labels for training examples

Plus a lot of computers…

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The human life is finite, but the knowledge is infinite. Learning from infinite examples by

applying PSA to 2nd Order SGD

is a good idea!

Thank you for your attention!http://aiia.iis.sinica.edu.twhttp://chunnan.iis.sinica.edu.tw/~chunnan

This research is supported mostly by NRPGM’s advanced bioinformatics core facility grant 2005-2011.