score_sde
Financial-Models-Numerical-Methods
| score_sde | Financial-Models-Numerical-Methods | |
|---|---|---|
| 6 | 3 | |
| 1,242 | 5,258 | |
| - | - | |
| 0.0 | 6.2 | |
| over 1 year ago | 2 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | GNU Affero General Public License v3.0 |
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?
Financial-Models-Numerical-Methods
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Pricing models - which do MMs use these days
If you have experience with Python, here is a great repo: https://github.com/cantaro86/Financial-Models-Numerical-Methods If you don't know Python, I suggest you learn. It's one of the easier languages to learn. The course below will teach you everything you need to get started. https://www.youtube.com/watch?v=rfscVS0vtbw
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Ask HN: Best Resources on (Computational) Finance
I found this collection of Jupyter notebooks really well done. Some basic knowledge in stochastic calculus, financial mathematics and statistics is needed.
https://github.com/cantaro86/Financial-Models-Numerical-Meth...
- Classical portfolio optimization in a Python notebook
What are some alternatives?
guided-diffusion
machine_learning_basics - Plain python implementations of basic machine learning algorithms
pytorch-generative - Easy generative modeling in PyTorch.
rmi - A learned index structure
SDE - Example codes for the book Applied Stochastic Differential Equations
Quantsbin - Quantitative Finance tools
Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - [ECCV 2022] Compositional Generation using Diffusion Models
fastpages - An easy to use blogging platform, with enhanced support for Jupyter Notebooks.
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
hca-resources - zipline-broker Examples. Full notebooks plus python code for long term investment strategies using zipline based tools.
score_sde_pytorch - PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
dynamax - State Space Models library in JAX