pytensor
fortuna
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| pytensor | fortuna | |
|---|---|---|
| 1 | 5 | |
| 244 | 850 | |
| 11.5% | 2.1% | |
| 9.5 | 8.5 | |
| 4 days ago | 9 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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pytensor
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[D] Programming language for developing computational statistics algorithms
I wouldn't overlook also how many goodies these existing PPLs are coming with. Starting from scratch means that one would have code up all the utilities to assess convergence, chain properties, etc. etc. on their own. And more advanced tricks like auto-differentiations that by themselves are huge perks are also unavailable (goodbye easy ADVI). For example PyMC, now uses Numba and JAX (via Aesara/PyTensor depending on your version), so theoretically (and practically) you can include arbitary PyTensor/JAX code in your model. I use PyMC as an example here, but this extends to most other PPLs too depending on their backends.
fortuna
- 🚀 AWS launches Fortuna, an open-source library for Uncertainty Quantification
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[P] 🚀 AWS launches Fortuna, an open-source library for Uncertainty Quantification
What is the best end-to-end example showing it? https://github.com/awslabs/fortuna/blob/main/examples/mnist_classification.ipynb ? It would be nice to have some visual explainer, as in https://github.com/aangelopoulos/conformal_classification .
- AWS Fortuna, an open-source library for Uncertainty Quantification
What are some alternatives?
stan - Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
surface_normal_uncertainty - (ICCV 2021 - oral) Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
uq-vae - Solving Bayesian Inverse Problems via Variational Autoencoders
pytorch-forecasting - Time series forecasting with PyTorch
deep-kernel-transfer - Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
jax-resnet - Implementations and checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX (Flax).
conformal_classification - Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
GradCache - Run Effective Large Batch Contrastive Learning Beyond GPU/TPU Memory Constraint