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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 121–130 of 2050 papers

TitleStatusHype
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
CNN Model & Tuning for Global Road Damage DetectionCode1
An Information-theoretic Approach to Distribution ShiftsCode1
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
clusterBMA: Bayesian model averaging for clusteringCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark StudyCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models CascadeCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
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