Jun 7, 2024 � Our main contribution is an online Cost-Aware Retraining Algorithm (CARA) that optimizes the trade-off between the two costs.
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Jul 9, 2024 � In this work, we study the decision problem of whether to retrain or keep an existing ML model based on the data, the model, and the predictive�...
Mar 18, 2024 � In this work, we study the decision problem of whether to retrain or keep an existing ML model based on the data, the model, and the predictive queries�...
Oct 9, 2023 � Our main contribution is an online Cost-Aware Retraining Algorithm (Cara) that optimizes the trade-off between the two costs. Cara is defined as�...
Oct 6, 2023 � Our main contribution is a Cost-Aware Retraining Algorithm called Cara, which optimizes the trade-off over streams of data and queries.
Jul 14, 2024 � To avoid repetitive and costly tuning in these cases, it is a common practice to use the same hyperparameter setting for every retraining [44,21]�...
MLFFs take a molecular configuration as input and then predict the forces on each atom in the molecule, consequently speeding up the force calculation step.
Missing: retraining | Show results with:retraining
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Oct 27, 2023 � TL;DR: We propose a framework, ASTEROID, to reduce the data cost of training machine learning force fields. Abstract:.
Cost-Aware Retraining for Machine Learning. Cost-Aware Retraining for Machine Learning. Year of publication. 2024. Authors. Mahadevan, Ananth; Mathioudakis�...
In this work, we first design a novel loss function that embeds the cost information for the training stage of cost-sensitive deep learning. We then show that�...
Missing: retraining | Show results with:retraining