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Showing 1 to 6 of 6 for “"Custom Loss"”.
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A Comparative Study of Machine Learning Models for Multivariate NextG Network Traffic Prediction with SLA-based Loss Function
… models in network traffic prediction using a custom Service-Level Agreement (SLA) - based loss function to ensure SLA violation constraints while minimizing overprovisioning. The proposed SLA-based parametric custom loss functions are used to maintain the SLA violation rate percentages the …
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Low-shot Visual Recognition
… to obey certain property by using a custom loss function. We believe that when the lowshot sample obey this property the classification step becomes easier. We show that the proposed solution performs better than the softmax classifier by a good margin.
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Methodologies for Systematic Evaluation and Targeted Mitigation of Deficiencies in Critical Machine Learning Models
… using two complementary strategies: (i) a custom loss function that penalizes violations of medical constraints, and (ii) a rule-based decision tree derived from clinical knowledge, aggregated with a data-driven model. The resulting knowledge-guided models demonstrated notable improvements …
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Application of Machine Learning Techniques to Forecast Harmful Algal Blooms in Gulf of Mexico
… in the grid within each data point, we use a custom loss function that ignores prediction errors on missing cells. Thus the loss function critiques the models based on known cells alone, while the models act with (forward/backward) predictions that are spatiotemporally consistent across both …
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Achieving Security and Reliability of Industrial Control Systems Using Data-Driven Models Informed by Physical Domain Knowledge
… graph neural network, trained using a custom loss function defined by the power flow model in addition to training data, to quickly localize attacks and adapt to different topologies. By embedding knowledge of the physics of the grid, the resulting data-driven model is able to transfer …
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Time-to-Event Prediction Using Deep Learning Models: Application to GPU Failure Data
… vectors. We utilize the Cross-Entropy Loss function to optimize TypeEmbedNet and the Mean Squared Error (MSE) for TimeEmbedNet. We develop evaluation metrics to comprehensively assess the models' performance in predicting both failure time and status, taking into account the …