What is it about?
Loss functions guide machine learning models towards concentrating on the error most important to improve upon. Power loss functions decrease the loss for confident predictions and increase the loss for error-prone predictions and enable the learning model to adapt to instance difficulty accordingly. Prediction models using power loss functions improve both AUC and F1.
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Why is it important?
Power loss functions allow the development of adaptive machine learning models that can adjust its learning according to the difficulty of instances and the confidence of the predictor as the learning progresses. It thereby fits the training data and the learning task better with performance improvements as measured by AUC and F1.
Perspectives
This is one of the most influential and impactful article I have written. It is impacting machine learning at its core, which are loss functions.
Ergun Biçici
Huawei Technologies Co Ltd
Read the Original
This page is a summary of: Power Loss Function in Neural Networks for Predicting Click-Through Rate, September 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3604915.3610658.
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