mit-gfx/ContinuousParetoMTL
[ICML 2020] Efficient Continuous Pareto Exploration in Multi-Task Learning
When training machine learning models to perform multiple tasks simultaneously, it's common to face trade-offs where improving one task degrades another. This project helps you efficiently find the best possible balance across these tasks. You provide your multi-task dataset and model, and it outputs a range of optimized models, each representing a different effective trade-off. This is ideal for machine learning practitioners and researchers working on real-world multi-objective optimization problems.
149 stars. No commits in the last 6 months.
Use this if you need to train a single model that performs well across multiple, potentially conflicting, objectives and want to explore the optimal compromises.
Not ideal if your model only has a single objective, or if you are not comfortable working with command-line tools and Python scripting for machine learning.
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Aug 12, 2021
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