Efficient-VDVAE
thinc
| Efficient-VDVAE | thinc | |
|---|---|---|
| 8 | 4 | |
| 184 | 2,798 | |
| - | 0.6% | |
| 0.0 | 7.6 | |
| almost 2 years ago | 7 days ago | |
| Python | Python | |
| MIT License | MIT License |
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Efficient-VDVAE
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Efficient-VDVAE: A SOTA open-source memory-efficient and stable very deep hierarchical VAE
Paper: https://arxiv.org/abs/2203.13751
- Show HN: Efficient-VDVAE an Open-source memory-efficient deep hierarchical VAE
- Efficient-VDVAE:Open-source memory-efficient deep hierarchical VAE
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[R] Efficient-VDVAE: An open-source memory-efficient and stable very deep hierarchical VAE
Code for https://arxiv.org/abs/2203.13751 found: https://github.com/Rayhane-mamah/Efficient-VDVAE
thinc
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
Agree, though I wouldn’t call PyTorch a drop-in for NumPy either. CuPy is the drop-in. Excepting some corner cases, you can use the same code for both. Thinc’s ops work with both NumPy and CuPy:
https://github.com/explosion/thinc/blob/master/thinc/backend...
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Tinygrad: A simple and powerful neural network framework
I love those tiny DNN frameworks, some examples that I studied in the past (I still use PyTorch for work related projects) :
thinc.by the creators of spaCy https://github.com/explosion/thinc
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good examples of functional-like python code that one can study?
thinc - defining neural nets in functional way jax, a new deep learning framework puts emphasis on functions rather than tensors, I've tested it for a couple of applications and it's really cool, you can write stuff like you'd write math expressions in papers using numpy. That speeds up development significantly, and makes code much more readable
- thinc - A refreshing functional take on deep learning, compatible with your favorite libraries
What are some alternatives?
disentangling-vae - Experiments for understanding disentanglement in VAE latent representations
quantulum3 - Library for unit extraction - fork of quantulum for python3
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
transferlearning - Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
code-representations-ml-brain - [NeurIPS 2022] "Convergent Representations of Computer Programs in Human and Artificial Neural Networks" by Shashank Srikant*, Benjamin Lipkin*, Anna A. Ivanova, Evelina Fedorenko, Una-May O'Reilly.
extending-jax - Extending JAX with custom C++ and CUDA code
HyperGAN - Composable GAN framework with api and user interface
dm-haiku - JAX-based neural network library
AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
textacy - NLP, before and after spaCy