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(Theoretical Background)
(Theoretical Background)
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** ref transition binary-quinary exp1 and exp2
** ref transition binary-quinary exp1 and exp2
* Deep Learning and Deep neural networks (DNN)
* Deep Learning and Deep neural networks (DNN)
== Deep Learning ==  
== Deep Learning ==  
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* Super Neural Networks
* Super Neural Networks
** What are they?
** What are they?
== Evolutionary algorithms ==  
== Evolutionary algorithms ==  

Revision as of 13:47, 21 March 2018




  • What is all this about?
  • Why should I read this thesis?
  • Is it any good?
  • What's new?


  • Who is your advisor?
  • Did anyone help you?
  • Who funded this work?
  • What's the name of your favorite pet?


From essay. More on multi task learning More on transfer learning

Raise problem: catastrophic forgetting.

Multiple solutions (PNN, PN, EWC)

  • Large structures (PNN, PN)
  • Limited in number of tasks it can retains(EWC)

Optimize reuse of knowledge while still providing valid solutions to tasks. More reuse and limited capacity use will increase amount of task a structure can learn.

where do i start?

Question DeepMind left unanswered is how different GAs influence task learning and module reuse. Exploration vs exploitation\ref{theoretic background on topic}

why this?

broad answers first, specify later. We know PN works. would it work better for different algorithms? logical next step from original paper "unit of evolution"


  • What do modular PN training do with the knowledge?
    • More/less accuracy?
    • More/less transferability?

Test by learning in end-to-end first then PN search. Difference in performance or reuse?

  • Can we make reuse easier by shifting focus of search algorithm?
    • PN original: Naive search. Higher exploitation improve on module selection?

How to answer?

  • Set up simple multitask scenarios and try.
    • 2 tasks where first are end to end vs PN
    • List algorithms with different selection pressure and try on multiple tasks.

Theoretical Background

Machine Learning

Intro about ML from the thesis \subsection{MLP and NN modeling as function approx} Inspired by the structure of the brain, the Neural Network (NN) consists of one or more layer where each layer is made up of perceptrons

  • What is a perceptron? How is it connected to input, output?
  • How is training done? Input against target
  • Multiple layer perceptron (MLP) as an artificial Neural Network (ANN).
    • Ref binary MNIST classification in exp 1
  • Backpropagation and optimizers (SGD and Adam)
    • ref binary MNIST/Quinary MNIST/exp2
  • Regression/function approximation (ReLU activation)
  • Classification (Softmax and probability approximation)
    • ref experiments
  • Image classification
    • ref experiments
  • Convolutional Neural Networks (CNN)
    • ref transition binary-quinary exp1 and exp2
  • Deep Learning and Deep neural networks (DNN)

Deep Learning

  • Feature extraction
    • Bigger black box
  • Network designs
  • Transfer learning
    • What is it?
    • Why do it?
    • How do it?
    • TL in CNNs
      • Who have done it?
      • Results?
      • Gabor approximation
  • Multi-task Learning
    • Curriculum Learning
      • ref to motivation behind task ordering in exp2
  • Catastrophic forgetting
      • EWC
      • PNN
      • PathNet
  • Super Neural Networks
    • What are they?

Evolutionary algorithms

  • What is it? Where does it come from?
  • Exploration vs Exploitation
    • ref experiments (formulated in the context of this trade-off)
  • Terms used in the evolutionary programming context
    • Population
    • Genotype and genome
    • Fitness-function
    • selection
    • recombination
    • generation
    • mutation
    • population diversity and convergence
  • Some types
    • GA
    • Evolutionary searches
    • short. Straight into tournament search
  • Tournament search
    • How it works, what are the steps?
    • Selection pressure (in larger context of EAs and then tournament search)
    • ref to search


Experiment 1: Search versus Selection

Experiment 2: Selection Pressure



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