wtte-rnn
GLOM-TensorFlow
| wtte-rnn | GLOM-TensorFlow | |
|---|---|---|
| 3 | 4 | |
| 759 | 36 | |
| - | - | |
| 0.0 | 0.0 | |
| over 3 years ago | about 3 years ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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wtte-rnn
- Pointers to reduce false negatives while not sacrificing accuracy in deep learning
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[R] apd-crs: Cure Rate Survival Analysis in Python
The typical reason you would go with a weibull function is if you want to be able to relax proportional hazard like in this work: https://github.com/ragulpr/wtte-rnn
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Predicting Hard Drive Failure with Machine Learning
You should check out this time-to-event neural network [1].
[1] https://github.com/ragulpr/wtte-rnn
GLOM-TensorFlow
- Implementing Geoffrey Hinton's latest paper
- Implementing Geoffrey Hinton's latest idea paper
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Implementing part-whole hierarchies in neural networks
I am glad to today present my attempt to implement Geoffery Hinton's latest idea paper about representing part-whole hierarchies in neural networks. Also doing ML more the way the human brain does it! https://github.com/Rishit-dagli/GLOM-TensorFlow
- Implementing part-whole hierarchies in Neural Nets
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