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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 1951–1960 of 2050 papers

TitleStatusHype
Deep Bayesian Multi-Target Learning for Recommender SystemsCode0
Recurrent Neural Networks for Fuzz Testing Web BrowsersCode0
A Deep Learning Method for Comparing Bayesian Hierarchical ModelsCode0
Variational Inference for Sparse Gaussian Process Modulated Hawkes ProcessCode0
Learning Conditional Invariance through Cycle ConsistencyCode0
Learning Counterfactual Representations for Estimating Individual Dose-Response CurvesCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
Learning diffusion coefficients, kinetic parameters, and the number of underlying states from a multi-state diffusion process: robustness results and application to PDK1/PKCα, dynamicsCode0
Learning Disentangled Discrete RepresentationsCode0
Sparse Interaction Neighborhood Selection for Markov Random Fields via Reversible Jump and PseudoposteriorsCode0
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