deepNOID
wtte-rnn
Our great sponsors
| deepNOID | wtte-rnn | |
|---|---|---|
| 1 | 3 | |
| 4 | 756 | |
| - | - | |
| 0.0 | 0.0 | |
| about 3 years ago | over 3 years ago | |
| Python | Python | |
| - | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
deepNOID
wtte-rnn
- Pointers to reduce false negatives while not sacrificing accuracy in deep learning
-
[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
-
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
What are some alternatives?
muzic - Muzic: Music Understanding and Generation with Artificial Intelligence
easyesn - Python library for Reservoir Computing using Echo State Networks
spektral - Graph Neural Networks with Keras and Tensorflow 2.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
DeepMalwareDetector - A Deep Learning framework that analyses Windows PE files to detect malicious Softwares.
GLOM-TensorFlow - An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data
Machine-Learning-Game-Ideas - Game ideas generation using neural networks
neptune-client - 📘 The MLOps stack component for experiment tracking
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
providence
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.