wtte-rnn
easyesn
wtte-rnn | easyesn | |
---|---|---|
3 | 1 | |
756 | 131 | |
- | 0.0% | |
0.0 | 2.6 | |
over 3 years ago | over 3 years ago | |
Python | Python | |
MIT License | - |
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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
easyesn
What are some alternatives?
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GLOM-TensorFlow - An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data
RNN-Twitter-Bot - 🤖✏️ A Twitter bot written in Python trained with a recurrent neural network.
neptune-client - 📘 The MLOps stack component for experiment tracking
ng-rc-paper-code - Code for the paper "Next Generation Reservoir Computing"
providence
handwriting-synthesis - Handwriting Synthesis with RNNs ✏️
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
DeepMalwareDetector - A Deep Learning framework that analyses Windows PE files to detect malicious Softwares.
rc - Reservoir Computing, an RNN flavor