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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 576–600 of 2050 papers

TitleStatusHype
Measuring Domain Shifts using Deep Learning Remote Photoplethysmography Model Similarity—0
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces—0
Model Selection with Model Zoo via Graph LearningCode0
Predictive Analytics of Varieties of PotatoesCode0
SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems—0
GLEMOS: Benchmark for Instantaneous Graph Learning Model SelectionCode0
Learning the mechanisms of network growthCode0
Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection—0
Bayesian Nonparametrics: An Alternative to Deep Learning—0
Individual Text Corpora Predict Openness, Interests, Knowledge and Level of Education—0
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields—0
Carbon Intensity-Aware Adaptive Inference of DNNs—0
Conformal online model aggregationCode0
An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced ClassificationCode0
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
DiTMoS: Delving into Diverse Tiny-Model Selection on MicrocontrollersCode0
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes—0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Pre-Trained Model Recommendation for Downstream Fine-tuning—0
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables—0
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits—0
Regularized DeepIV with Model Selection—0
A data-centric approach to class-specific bias in image data augmentation—0
Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
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