score_sde
best-of-ml-python
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| score_sde | best-of-ml-python | |
|---|---|---|
| 6 | 16 | |
| 1,242 | 15,335 | |
| - | 1.5% | |
| 0.0 | 7.8 | |
| over 1 year ago | 3 days ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | Creative Commons Attribution Share Alike 4.0 |
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.
score_sde
- Ask HN: How to get back into AI?
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[D] Variance of sampling in diffusion models
Perhaps the ODE interpretation would be helpful (see here and here) which turns DDPMs into neural ODEs using the Fokker-Planck equation so after the initial starting noise, the sampling process is deterministic. If samples are noisy even with the full number of steps then you might need to increase the number of steps further.
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[D] Why is the diffution model so powerful? but the math behind it is so simple.
Turns out that diffusion models also define a certain differential equation, making it a neural ODE. Then you can just integrate the ODE in the other direction to get the exact inverse for the DDPM (it's not entirely exact b/c of numerical error in the solver, but close enough)
- [D] Are DDPMs a variation on Score Based Generative Modeling? Or is there a fundemental difference between the two?
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Diffusion Models Beat GANs on Image Synthesis
This new approach to generative modelling looks very intriguing.
In a similar ilk, there's this ICLR paper from this year using stochastic differential equations for generative modelling: https://arxiv.org/abs/2011.13456
- [D] Efficient, concurrent input pipelines in JAX?
best-of-ml-python
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Ask HN: How to get back into AI?
For Python, here's a nice compilation: https://github.com/ml-tooling/best-of-ml-python/blob/main/RE...
- Best-Of Machine Learning with Python
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Questions regarding Job Requirements for data analyst to data science transition?
You will need numpy, scipy, pandas, scikit-learn, Keras/tensorflow/pytorch, xgboost and many many many others. See this list for example.
- Awesome list of ML
- Are there any speech recognition modules so I can write one and do not have to rely on google and the likes?
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Learning opencv
Take a look at this list on github. It has a pretty comprehensive list of python image libraries.
- Best-of Machine Learning with Python
- 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
What are some alternatives?
guided-diffusion
Awesome-WAF - 🔥 Web-application firewalls (WAFs) from security standpoint.
pytorch-generative - Easy generative modeling in PyTorch.
ktrain - ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
SDE - Example codes for the book Applied Stochastic Differential Equations
dtale - Visualizer for pandas data structures
Financial-Models-Numerical-Methods - Collection of notebooks about quantitative finance, with interactive python code.
ffcv - FFCV: Fast Forward Computer Vision (and other ML workloads!)
Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - [ECCV 2022] Compositional Generation using Diffusion Models
awesome-python - An opinionated list of awesome Python frameworks, libraries, software and resources.
score_sde_pytorch - PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data