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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 126–150 of 2050 papers

TitleStatusHype
ProbVLM: Probabilistic Adapter for Frozen Vision-Language ModelsCode1
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion ModelsCode1
Automating Outlier Detection via Meta-LearningCode1
BarcodeBERT: Transformers for Biodiversity AnalysisCode1
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models CascadeCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian QuadratureCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
BayesOpt Adversarial AttackCode1
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM EvaluationCode1
BERTScore: Evaluating Text Generation with BERTCode1
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
A network approach to topic modelsCode1
Can We Characterize Tasks Without Labels or Features?Code1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
CNN Model & Tuning for Global Road Damage DetectionCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataCode1
An information criterion for automatic gradient tree boostingCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
Assumption-lean inference for generalised linear model parametersCode1
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