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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1311–1320 of 2050 papers

TitleStatusHype
On the Limitation and Experience Replay for GNNs in Continual Learning—0
Towards Safer Online Spaces: Simulating and Assessing Intervention Strategies for Eating Disorder Discussions—0
Towards Stable and Comprehensive Domain Alignment: Max-Margin Domain-Adversarial Training—0
Towards Transferable Adversarial Perturbations with Minimum Norm—0
Towards trustworthy explanations with gradient-based attribution methods—0
Towards Typologically Aware Rescoring to Mitigate Unfaithfulness in Lower-Resource Languages—0
Towards Unsupervised Validation of Anomaly-Detection Models—0
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
Training Deep Neural Networks for Wireless Sensor Networks Using Loosely and Weakly Labeled Images—0
Training Machine Learning Models to Characterize Temporal Evolution of Disadvantaged Communities—0
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