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Awesome GitHub RepositoriesGame-Theoretic Loss Balancing
Techniques that use game theory to dynamically weight multiple task losses and prevent gradient dominance.
Distinct from Multi-Task Learning Models: Specifically addresses the mathematical balancing of loss functions in multi-task settings
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Awesome Game-Theoretic Loss Balancing GitHub Repositories
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- uber/ludwig
uber/ludwig
11,718在 GitHub 上查看Ludwig is a declarative machine learning framework designed for training neural networks and large language models using configuration files instead of manual coding. It functions as a multimodal model builder and a low-code tool for supervised fine-tuning, allowing users to build models that process mixed inputs of text, images, audio, and tabular data. The project distinguishes itself through an automated hyperparameter optimizer and a system for large language model fine-tuning using parameter-efficient adapters. It features a multimodal data pipeline and the ability to automatically gener
Optimizes multi-task loss functions using game-theoretic methods to prevent any single task from dominating the gradient.
Python
