PyTorchZeroToAll
coral-cnn
| PyTorchZeroToAll | coral-cnn | |
|---|---|---|
| 1 | 4 | |
| 3,825 | 330 | |
| - | 0.0% | |
| 0.0 | 0.0 | |
| almost 2 years ago | almost 3 years ago | |
| Python | Python | |
| - | MIT License |
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PyTorchZeroToAll
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[D] I'm trying to do more stuff in pure Tensorflow. Is there an in-depth book that explain constructing recurrent, convolutional, graph etc layers in it?
I'm doing this rn, but with PyTorch. I look for notebooks/scripts (https://github.com/hunkim/PyTorchZeroToAll, primarily) on github read and copy them, along with the deeplearning book. Surprisingly pretty much everything just clicks now, it's my third attempt reading the text though. I don't think any book serves the purpose well, except for knowing the well established conventions of the field.
coral-cnn
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[D] Why is Ordinal Regression so overlooked?
The most recent and usable DL attempt I have found is the CORAL/CORN frameworks (keras, pytorch) which have just a few stars, and that's it.
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[D] can regression models be used for ranking?
To your question, there are specific types of models called ordinal regression / ordinal classification models that do not assume a metric distance between values. E.g., if you have "20/hr, $15/hr, $0/hr" these models don't assume that the distance between 0 and 15 is 3x the distance between 20 and 15. It just assumes 20 > 15 > 0. We worked on this a bit in the context of neural networks: https://www.sciencedirect.com/science/article/pii/S016786552030413X , https://raschka-research-group.github.io/coral_pytorch/
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[D] Modeling class errors
If you are interested, I recently worked on a simple ordinal regression approach for neural networks here: https://www.sciencedirect.com/science/article/pii/S016786552030413X
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[R] [D] What machine learning methods can be used for ordinal regression?
Just took a quick look at that paper, it sounds like a good approach. If you are interested, we recently developed an ordinal regression approach with implementation in PyTorch (https://github.com/Raschka-research-group/coral-cnn). Someone also recently ported it to Keras: https://github.com/ck37/coral-ordinal. I haven't read the paper you mentioned in detail, but it seems our method is similar except that we add the probabilities that are >0.5 and that we have theoretical guarantees. rank consistency.
What are some alternatives?
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coral-ordinal - Tensorflow Keras implementation of ordinal regression using consistent rank logits (CORAL) by Cao et al. (2019)
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