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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 591–600 of 2050 papers

TitleStatusHype
Differentiable Model Selection for Ensemble LearningCode0
Adaptive spline fitting with particle swarm optimizationCode0
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity ModelsCode0
Impact of ImageNet Model Selection on Domain AdaptationCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Dirichlet process mixtures of block g priors for model selection and prediction in linear modelsCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Deep Bayesian Multi-Target Learning for Recommender SystemsCode0
tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)Code0
Deep Active Learning with Adaptive AcquisitionCode0
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