Alternate Loss Functions for Classification and Robust Regression Can Improve the Accuracy of Artificial Neural Networks. All machine learning algorithms use a loss, cost, utility or reward function to encode the learning objective and oversee the learning process.
Mar 17, 2023
Nov 5, 2024 · This paper shows that training speed and final accuracy of neural networks can signif- icantly depend on the loss function used to train neural ...
Sep 4, 2024 · This paper shows that training speed and final accuracy of neural networks can significantly depend on the loss function used to train neural ...
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Mar 17, 2023 · This paper shows that training speed and final accuracy of neural networks can significantly depend on the loss function used to train neural ...
This paper shows that training speed and final accuracy of neuralnetworks can significantly depend on the loss function used to train ...
Mar 8, 2023 · Is it possible to train a neural network that modulates its own loss function, as well as the hyperparameters of its training like momentum?
Missing: Improve Performance
Nov 5, 2024 · This paper demonstrates that the choice of loss function can have a significant impact on the training and performance of neural networks. By ...
Apr 12, 2019 · One way out that avoids local restarts is to alternate between different loss (error measurement) functions during evolution. For a neural ...
Missing: Performance | Show results with:Performance
In this work, we concentrate our study on the inductive bias occurring when minimizing the cross-entropy loss with different batch sizes and learning rates.
Our objective is to enhance model performance through refining these loss functions. Existing loss functions in sequential recommendation are discussed and ...