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2020-02-17 07:34:39 +08:00
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@@ -59,7 +59,7 @@ Results on [*Smartmeter* prediction](./smartmeters-ANP-RNN.ipynb) (lower is bett
|ANP-RNN|-1.27|0.0047|
|ANP|-1.3|0.0072|
|NP|-1.3|0.0040|
|LSTM|-0.78| 0.0074 |
|LSTM|-0.78| 0.0074 |
### Example LSTM baseline
@@ -202,3 +202,18 @@ A list of projects I used as reference or modified to make this one:
- If you want to try vanilla neural processes: https://github.com/EmilienDupont/neural-processes/blob/master/example-1d.ipynb
I'm very grateful for all these authors for sharing their work. It was a pleasure to dive deep into these models compare the different implementations.
Neural process papers:
- [2019, Attentive Neural Processes](https://arxiv.org/abs/1910.09323) (using attention to prevent underfitting)
- [2019, Functional Neural Processes](https://arxiv.org/abs/1906.08324)
- [2019, Recurrent Neural Processes](https://arxiv.org/abs/1906.05915) (2d and 3d over time)
- [2019, Spatiotemporal Modeling using Recurrent
Neural Processes](https://www.ri.cmu.edu/wp-content/uploads/2019/08/msr_thesis_document.pdf) (infilling spatial information, using a RNN for time information, no code)
- [2018, Conditional Neural Processes](https://arxiv.org/abs/1807.01613) [code](https://github.com/deepmind/neural-processes)
- [2018, Neural Processes](https://arxiv.org/abs/1807.01622)
Blogposts:
- [2018, Neural Processes as distributions over functions
](https://kasparmartens.rbind.io/post/np/)