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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 631–640 of 2050 papers

TitleStatusHype
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks—0
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
Online Laplace Model Selection Revisited—0
Deep learning for dynamic graphs: models and benchmarksCode1
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts—0
Bayesian taut splines for estimating the number of modes—0
Action-State Dependent Dynamic Model Selection—0
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?—0
Learning Lie Group Symmetry Transformations with Neural NetworksCode0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
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