User:Martijho
From Robin
(Difference between revisions)
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== Who cites PathNet? == | == Who cites PathNet? == | ||
| - | * [https://arxiv.org/pdf/1703.10371.pdf| Born to Learn:] | + | * [https://arxiv.org/pdf/1703.10371.pdf | Born to Learn:] |
EPANN - Evolved Plastic Artificial Neural Networks | EPANN - Evolved Plastic Artificial Neural Networks | ||
Mentions Pathnet as an example of where evolution where | Mentions Pathnet as an example of where evolution where | ||
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evolution and deep learning approaches. | evolution and deep learning approaches. | ||
| - | * [https://arxiv.org/pdf/1706.00046.pdf| Learning time-efficient deep architectures with budgeted super networks] | + | * [https://arxiv.org/pdf/1706.00046.pdf | Learning time-efficient deep architectures with budgeted super networks] |
Mentions PathNet as a predecessor in the super neural network family | Mentions PathNet as a predecessor in the super neural network family | ||
| - | * [https://arxiv.org/pdf/1708.07902.pdf| Deep Learning for video game playing] | + | * [https://arxiv.org/pdf/1708.07902.pdf | Deep Learning for video game playing] |
Reviewing recent deep learning advances in the context | Reviewing recent deep learning advances in the context | ||
of how they have been applied to play different types of video games | of how they have been applied to play different types of video games | ||
| - | *[http://ceur-ws.org/Vol-1958/IOTSTREAMING2.pdf| Evolutive deep models for online learning on data streams with no storage] | + | *[http://ceur-ws.org/Vol-1958/IOTSTREAMING2.pdf | Evolutive deep models for online learning on data streams with no storage] |
Revision as of 13:02, 6 November 2017
PathNet
- Super neural network
- Evolved sub-models from a larger set of parameters
- Multitask learning
- No catastrophic forgetting
- Embedded transfer learning
Who cites PathNet?
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.
Mentions PathNet as a predecessor in the super neural network family
Reviewing recent deep learning advances in the context of how they have been applied to play different types of video games
