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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 121–130 of 2050 papers

TitleStatusHype
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
Rich Feature Construction for the Optimization-Generalization DilemmaCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think AgainCode1
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification TasksCode1
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
Invariance Learning in Deep Neural Networks with Differentiable Laplace ApproximationsCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing dataCode1
On Evaluation Metrics for Graph Generative ModelsCode1
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