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Learning to learn
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Achievements

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

Achievement: Pull Sharkx2Achievement: QuickdrawAchievement: StarstruckAchievement: YOLOAchievement: Arctic Code Vault Contributor
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@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: 16 May 2024 - To: 23 May 2024

Python             5 hrs 31 mins   █████████████████████▓░░░   86.06 %
Other              40 mins         ██▓░░░░░░░░░░░░░░░░░░░░░░   10.55 %
XML                9 mins          ▓░░░░░░░░░░░░░░░░░░░░░░░░   02.47 %
TeX                2 mins          ░░░░░░░░░░░░░░░░░░░░░░░░░   00.57 %
BibTeX             1 min           ░░░░░░░░░░░░░░░░░░░░░░░░░   00.28 %

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Pinned

  1. BP_MLL_Pytorch BP_MLL_Pytorch Public

    PyTorch Version of BP MLL loss function for multilabel classification

    Python 12

  2. Atom2Vec Atom2Vec Public

    Atom2Vec: a simple way to describe atoms for machine learning

    Python 30 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 15 6