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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 776–800 of 2050 papers

TitleStatusHype
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks—0
Online Laplace Model Selection Revisited—0
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts—0
Bayesian taut splines for estimating the number of modes—0
Action-State Dependent Dynamic Model Selection—0
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?—0
Learning Lie Group Symmetry Transformations with Neural NetworksCode0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Confidence-based Ensembles of End-to-End Speech Recognition Models—0
Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning—0
Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications—0
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts—0
Frame Fusion with Vehicle Motion Prediction for 3D Object Detection—0
Human Limits in Machine Learning: Prediction of Plant Phenotypes Using Soil Microbiome DataCode0
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization—0
Sliding Window Neural Generated Tracking Based on Measurement Model—0
Two-level histograms for dealing with outliers and heavy tail distributions—0
Gibbs-Based Information Criteria and the Over-Parameterized Regime—0
Stochastic Marginal Likelihood Gradients using Neural Tangent KernelsCode0
Data-Driven Online Model Selection With Regret Guarantees—0
Bivariate Causal Discovery using Bayesian Model SelectionCode0
Structured model selection via _1-_2 optimizationCode0
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint AveragingCode0
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