Focus on Unsupervised Learning. No teacher specifying right answer

Focus on Unsupervised Learning

No teacher specifying right answer

No teacher specifying right answerTechniques for autonomous SW or

robots to learn to characterize their sensations

“Competitive” learning algorithm

“Competitive” learning algorithm Winner-take-all

Learning Rule: Iterate

Learning Rule: IterateFind “winner”

Learning Rule: IterateFind “winner”Delta = learning rate * (sample –

prototype)

Example: Learning rate = .05 Sample = (122, 180) Winner = (84, 203)

DeltaX = learning rate * (sample x – winner x) DeltaX = .05 * (122 – 84) DeltaX = 1.9 New prototype x value = 84 + 1.9 = 85.9

DeltaY = .05 * (180 - 203) DeltaY = -1.15 New prototype y value = 203 -1.15 = 201.85

Python Demo

Sound familiar?

ClusteringDimensionality ReductionData visualization

Yves Amu Klein’s Octofungi uses a kohonen neural network to react to its environment

Associative learning method

Associative learning methodBiologically inspired

Associative learning methodBiologically inspiredBehavioral conditioning and

Psychological models

activation = sign(input sum)

activation = sign(input sum)+1 and -1 inputs

activation = sign(input sum)+1 and -1 inputs2 layers

weight change = learning constant *

neuron A activation * neuron B activation

weight change = learning constant *

desired output * input value

Long-term memory

Long-term memory Inspired by Hebbian learning

Long-term memory Inspired by Hebbian learningContent-addressable memory

Long-term memory Inspired by Hebbian learningContent-addressable memoryFeedback and convergance

Attractor – “a state or output vector in a system

towards which the system consistently evolves toward given a specific input vector.”

Attractor Basin – “the set of input vectors surrounding

a learned vector which will converge to the same output vector.”

Bi-directional Associative MemoryAttractor network with 2 layers

Smell Taste

Bi-directional Associative MemoryAttractor network with 2 layers Information flows in both directions

Bi-directional Associative MemoryAttractor network with 2 layers Information flows in both directionsMatrix worked out in advance

Hamming vector – vector composed of

+1 and -1 only

Ex. [1,-1,-1,1] [1,1,-1,1]

Hamming distance – number of components by which 2 vectors differ

Ex. [1,-1,-1,1] and [1,1,-1,1]Differ in only one element (index 1)Hamming distance = 1

Weights are a matrix based on memories we want to store

To associate X = [1,-1,-1,-1] With Y = [-1,1,1]XY

1 -1 -1 -1

-1 -1 1 1 1

1 1 -1 -1 -1

1 1 -1 -1 -1

[1,-1,-1,-1] -> [1,1,1] and [-1,-1,-1,1] -> [1,-1,1]

+

=

1 -1 -1 -1

1 -1 -1 -1

1 -1 -1 -1

-1 -1 -1 1

1 1 1 -1

-1 -1 -1 1

0 -2 -2 0

2 0 0 -2

0 -2 -2 0

AutoassociativeRecurrent

To remember the pattern [1,-1,1,-1,1]

1 -1 1 -1 11 1 -1 1 -1 1-1 -1 1 -1 1 -11 1 -1 1 -1 1-1 -1 1 -1 1 -11 1 -1 1 -1 1

Demo

Complements of a vector also become attractors

Complements of a vector also become attractors

Ex. Installing [1,-1, 1] [-1, 1, -1] also

“remembered”

Complements of a vector also become attractors

Crosstalk

George Christos “Memory and Dreams”

Ralph E. Hoffman models of schizophrenia

Spurious Memories