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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 501–525 of 2050 papers

TitleStatusHype
Bayesian Model Selection via Mean-Field Variational Approximation—0
Random Models for Fuzzy Clustering Similarity Measures—0
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation—0
Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue—0
Predictive variational autoencoder for learning robust representations of time-series data—0
Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey—0
Evaluating the Utility of Model Explanations for Model Development—0
Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformers—0
Deep Bayes Factors—0
Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework—0
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion ModelsCode1
Towards Measuring Representational Similarity of Large Language ModelsCode0
Approximation of Intractable Likelihood Functions in Systems Biology via Normalizing Flows—0
How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model RankingCode0
Risk-Controlling Model Selection via Guided Bayesian Optimization—0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification—0
Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects—0
Task-Distributionally Robust Data-Free Meta-Learning—0
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection—0
Improved identification accuracy in equation learning via comprehensive R^2-elimination and Bayesian model selectionCode0
Machine-Guided Discovery of a Real-World Rogue Wave ModelCode1
GPT in Data Science: A Practical Exploration of Model Selection—0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework—0
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