SOTAVerified

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
DeSocial: Blockchain-based Decentralized Social NetworksCode1
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster ManagementCode1
Empirical evaluation of scoring functions for Bayesian network model selectionCode1
Deep learning for dynamic graphs: models and benchmarksCode1
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark StudyCode1
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models CascadeCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
DriveML: An R Package for Driverless Machine LearningCode1
Assumption-lean inference for generalised linear model parametersCode1
A stacked DCNN to predict the RUL of a turbofan engineCode1
A network approach to topic modelsCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD GeneralizationCode1
An information criterion for automatic gradient tree boostingCode1
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing dataCode1
Data Models for Dataset Drift Controls in Machine Learning With Optical ImagesCode1
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