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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 401–425 of 2050 papers

TitleStatusHype
Automated Model Selection for Generalized Linear Models—0
Estimating the Number of Components in Finite Mixture Models via Variational Approximation—0
On uncertainty-penalized Bayesian information criterion—0
Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory—0
A Sentiment Analysis of Medical Text Based on Deep Learning—0
A Realistic Protocol for Evaluation of Weakly Supervised Object LocalizationCode0
On the Necessity of Collaboration for Online Model Selection with Decentralized Data—0
Semantic Approach to Quantifying the Consistency of Diffusion Model Image GenerationCode0
Measuring Domain Shifts using Deep Learning Remote Photoplethysmography Model Similarity—0
The CAST package for training and assessment of spatial prediction models in RCode2
Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal InputsCode2
Model Selection with Model Zoo via Graph LearningCode0
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces—0
Predictive Analytics of Varieties of PotatoesCode0
GLEMOS: Benchmark for Instantaneous Graph Learning Model SelectionCode0
SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems—0
Learning the mechanisms of network growthCode0
Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection—0
Individual Text Corpora Predict Openness, Interests, Knowledge and Level of Education—0
Bayesian Nonparametrics: An Alternative to Deep Learning—0
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields—0
Carbon Intensity-Aware Adaptive Inference of DNNs—0
An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced ClassificationCode0
Conformal online model aggregationCode0
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
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