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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 981–990 of 2050 papers

TitleStatusHype
On Evaluation Metrics for Graph Generative ModelsCode1
The Time-Varying Multivariate Autoregressive Index Model—0
Evaluation of HTR models without Ground Truth MaterialCode0
One Step Is Enough for Few-Shot Cross-Lingual Transfer: Co-Training with Gradient Optimization—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics—0
Problem-dependent attention and effort in neural networks with applications to image resolution and model selection—0
Self-directed Machine Learning—0
Have I done enough planning or should I plan more?Code0
Deep Learning and Linear Programming for Automated Ensemble Forecasting and InterpretationCode0
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