User:Martijho
From Robin
(Difference between revisions)
(Ny side: == PathNet == * Super neural network * Evolved sub-models from a larger set of parameters * Multitask learning * No catastrophic forgetting * Embedded transfer learning == Who cites Path…) |
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== PathNet == | == PathNet == | ||
* Super neural network | * Super neural network | ||
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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 | + | Mentions Pathnet as an example of where evolution where |
used to train a network on multiple tasks. "While these | used to train a network on multiple tasks. "While these | ||
results were only possible through significant computational | results were only possible through significant computational | ||
resources, they demonstrate the potential of combining | resources, they demonstrate the potential of combining | ||
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] | ||
| + | Mentions PathNet as a predecessor in the super neural network family | ||
| + | |||
| + | * [https://arxiv.org/pdf/1708.07902.pdf| 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 | ||
| + | |||
| + | *[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
