User:Martijho
From Robin
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=== ''[https://openreview.net/pdf?id=H1XLbXEtg Online multi-task learning using active sampling]'' === | === ''[https://openreview.net/pdf?id=H1XLbXEtg Online multi-task learning using active sampling]'' === | ||
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| + | === ''[http://juxi.net/workshop/deep-learning-rss-2017/papers/Xu.pdf Hierarchical Task Generalization | ||
| + | with Neural Programs]'' === | ||
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| + | === ''[https://arxiv.org/pdf/1702.02217.pdf Multitask Evolution with Cartesian Genetic Programming | ||
| + | ]'' === | ||
Revision as of 13:14, 6 November 2017
Contents |
PathNet
- Super neural network
- Evolved sub-models from a larger set of parameters
- Multitask learning
- No catastrophic forgetting
- Embedded transfer learning
Who cites PathNet?
Born to Learn
EPANN - Evolved Plastic Artificial Neural Networks Mentions Pathnet as an example of where evolution where used to train a network on multiple tasks. "While these results were only possible through significant computational resources, they demonstrate the potential of combining evolution and deep learning approaches.
Learning time-efficient deep architectures with budgeted super networks
Mentions PathNet as a predecessor in the super neural network family
Deep Learning for video game playing
Reviewing recent deep learning advances in the context of how they have been applied to play different types of video games
Evolutive deep models for online learning on data streams with no storage
Pathnet is proposed alongside PNNS as a way to deal with changing environments. It is mentioned that both PathNet and progressive networks show good results on sequences of tasks and are a good alternative to fine-tuning to accelerate learning.
Online multi-task learning using active sampling
=== [http://juxi.net/workshop/deep-learning-rss-2017/papers/Xu.pdf Hierarchical Task Generalization with Neural Programs] ===
=== [https://arxiv.org/pdf/1702.02217.pdf Multitask Evolution with Cartesian Genetic Programming ] ===
