prompt-tuning
fortuna
| prompt-tuning | fortuna | |
|---|---|---|
| 1 | 5 | |
| 623 | 851 | |
| 1.1% | 1.4% | |
| 5.7 | 8.2 | |
| 5 months ago | 14 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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prompt-tuning
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Need advice on learning to apply techniques from research papers.
For instance, right now I want to try applying the prompt-tuning technique on simple classification tasks. The github page looks as follows: https://github.com/google-research/prompt-tuning.
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?
flaxmodels - Pretrained deep learning models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet, etc.
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