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### 🚀 The feature, motivation and pitch
Fuyou Training Framework Integration for PyTorch
Description:
Integrate the Fuyou training framework into PyTorch to enable efficient fine-tuning of larg…
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I'm encountering an unexpected behavior while using the autograd_minimize function in Python for optimization. Here's a summary of the issue:
Objective: I'm using autograd_minimize to minimize an o…
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Hello 👋,
We're proposing an in-depth comparison between Next.js and Nuxt.js, two popular frameworks for building server-side rendered (SSR) and statically generated (SSG) web applications.
The c…
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### Feature Request Type
- [ ] Core functionality
- [ ] Add-on hardware support (eg. audio, RGB, OLED screen, etc.)
- [X] Alteration (enhancement/optimization) of existing feature(s)
- [ ] New behavi…
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## Rationale
MOOSE could be and should be faster. I believe much of the work needed to make that happen can be done entirely at the libMesh level, but there will be cases (where MOOSE itself is …
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Currently we use only the baseline move-prover generated stackless bytecode as input to our translation. However, the prover also provides a number of potentially useful analyses and optimizations tha…
nvjle updated
10 months ago
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luajit does some insane optimization shit that would be a good to test on our framework.
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Investigate whether using something like [optuna](https://optuna.org) is better than plain ol' grid search when it comes to the non-deep-learning algorithms that are in SKLL/scikit-learn.
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https://arxiv.org/pdf/1907.10902.pdf
https://github.com/pfnet/optuna/
SIGKDD'19 Applied Data Science Track Papers
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