Martijho-PathNet-thesis
From Robin
(Difference between revisions)
Line 13: | Line 13: | ||
= Introduction = | = Introduction = | ||
+ | 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" | ||
+ | |||
+ | == Problem/hypothesis == | ||
+ | * 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 = | = Theoretical Background = |
Revision as of 13:37, 21 March 2018
Contents |
Opening
Abstract
- What is all this about?
- Why should I read this thesis?
- Is it any good?
- What's new?
Acknowledgements
- Who is your advisor?
- Did anyone help you?
- Who funded this work?
- What's the name of your favorite pet?
Introduction
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"
Problem/hypothesis
- 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.