coral-ordinal
coral-cnn
| coral-ordinal | coral-cnn | |
|---|---|---|
| 2 | 4 | |
| 75 | 330 | |
| - | 2.1% | |
| 0.0 | 0.0 | |
| about 2 years ago | about 3 years ago | |
| Python | Python | |
| MIT License | MIT License |
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coral-ordinal
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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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Hey all, I'm Sebastian Raschka, author of Machine Learning with Pytorch and Scikit-Learn. Please feel free to ask me anything!
Also, I often need to do some custom stuff for my research projects. E.g., take CORAL and CORN as an example (https://raschka-research-group.github.io/coral-pytorch/). Here, I needed custom losses and slight modifications to the forward pass. This was relatively easy to do in PyTorch. Someone was so kind to port it to TensorFlow/Keras (https://github.com/ck37/coral-ordinal/tree/master/coral_ordinal), but the code is much more complicated. For research and tinkering, I much prefer working with PyTorch.
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?
InvoiceNet - Deep neural network to extract intelligent information from invoice documents.
datatap-python - Focus on Algorithm Design, Not on Data Wrangling
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
contrastive-unpaired-translation - Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
corn-ordinal-neuralnet - Code and experiments for "Deep Neural Networks for Rank Consistent Ordinal Regression based on Conditional Probabilities"
PyTorchZeroToAll - Simple PyTorch Tutorials Zero to ALL!
Ordinal_Classifier - Introduce order in your classification within 1 line
ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models
NeuralNetworks - Implementation of a Neural Network that can detect whether a video is in-game or not
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. [Moved to: https://github.com/horovod/horovod]