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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 1841–1850 of 2050 papers

TitleStatusHype
BiasBed - Rigorous Texture Bias EvaluationCode0
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and L_0 RegularizationCode0
Multi-view Deep Subspace Clustering NetworksCode0
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov modelsCode0
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect EstimatorsCode0
Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility ReportCode0
Impact of ImageNet Model Selection on Domain AdaptationCode0
Exploring validation metrics for offline model-based optimisation with diffusion modelsCode0
Supervised Models Can Generalize Also When Trained on Random LabelCode0
Improved identification accuracy in equation learning via comprehensive R^2-elimination and Bayesian model selectionCode0
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