Yejin Kim

Study it and apply all!
Like to see error code because it is much better than warrning!

[Paper] AdaGDA, Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization

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This is a brief review for “AdaGDA, Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization”. You can see the paper at this archive link.

Paper Review post Notion Link

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[Survey] Recent technical reports

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This is a collection of recent technical reports from several vendors including Google DeepMind, x.AI, AllenAI, AI21Labs, Databricks and HyperCLOVA.

[Survey] Recent approaches about Super alignments

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This is a collection of recent approaches and papers about super-alignment and relevant topics.

[Survey] Recent approaches about Efficient ML

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This is a collection of recent approaches and papers about Efficient ML including Parameter Efficient Fine Tuning(PEFT), qunatization, pruning and other topi...

[Paper] Using Sliced Mutual Information to Study Memorization and Generalization

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Using Sliced Mutual Information to Study Memorization and Generalization PDF Link

Yejin Kim

[Paper] AdaGDA, Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization

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참고

[Survey] Recent technical reports

[Survey] Recent approaches about Super alignments

[Survey] Recent approaches about Efficient ML

[Paper] Using Sliced Mutual Information to Study Memorization and Generalization