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Learning to learn
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Learning to learn
41 followers · 23 following

Achievements

Achievement: Pull Sharkx2Achievement: QuickdrawAchievement: StarstruckAchievement: YOLOAchievement: Arctic Code Vault Contributor

Achievements

Achievement: Pull Sharkx2Achievement: QuickdrawAchievement: StarstruckAchievement: YOLOAchievement: Arctic Code Vault Contributor

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Organizations

@CederGroupHub @COVID-19-Text-Mining

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idocx/README.md
idocx's Github Stats

Yuxing believes firmly in the saying "With four parameters I can fit an elephant, and with five I can make him wiggle his trunk" by Enrico Fermi. [1] So, he devotes his entire though short research career to tuning five hypermeters of a model and is convinced that he will finally make it outperform any SOTA models like GPT-3 and AlphaFold 2.

Yuxing is not a computer scientist, a physicist or a chemist, but a HYPERPARAMETER TUNING SCIENTIST. "That makes a lot of difference. Hyperparameter tuning is the technique that changes our life, especially before pulishing a paper.", he said.

[1] Dyson, F. A meeting with Enrico Fermi. Nature 427, 297 (2004).

📊 Weekly development time

From: 03 November 2024 - To: 10 November 2024

Python   16 hrs 27 mins  ███████████████████████▒░   92.83 %
Other    54 mins         █▒░░░░░░░░░░░░░░░░░░░░░░░   05.08 %
C++      22 mins         ▓░░░░░░░░░░░░░░░░░░░░░░░░   02.09 %
TOML     0 secs          ░░░░░░░░░░░░░░░░░░░░░░░░░   00.00 %

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  1. BP_MLL_Pytorch BP_MLL_Pytorch Public

    PyTorch Version of BP MLL loss function for multilabel classification

    Python 14

  2. Atom2Vec Atom2Vec Public

    Atom2Vec: a simple way to describe atoms for machine learning

    Python 35 9

  3. CederGroupHub/alab_control CederGroupHub/alab_control Public

    Python 4 2

  4. CederGroupHub/alabos CederGroupHub/alabos Public

    AlabOS: Managing the workflows in the Autonomous lab

    Python 34 8