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ML researcher seeks advice on ablation studies without retraining

A machine learning researcher is seeking advice on how to conduct ablation studies for a paper without retraining a model from scratch. The concern is that retraining could introduce randomness, leading to results that don't accurately reflect the impact of removed components compared to the original trained model. The researcher is looking for methods or publication examples that address this challenge, aiming to perform ablations on an already saved checkpoint. AI

IMPACT Researchers often face challenges in methodology; sharing best practices for ablation studies can improve the rigor of AI research.

RANK_REASON The cluster consists of a user asking for advice on a common research methodology, not a new release or significant event.

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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Plane_Stick8394 ·

    How Do You Handle Ablation Studies When the Original Model Is Already Trained?[R]

    <!-- SC_OFF --><div class="md"><p>I'm running into an issue with an ablation study for a paper I'm preparing. I trained a model. The model achieved my best result, and I saved the trained checkpoint (<code>.pth</code> file). Now my supervisor wants me to perform an ablation study…