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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 281–290 of 2050 papers

TitleStatusHype
Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text RankingCode0
Eagle: Efficient Training-Free Router for Multi-LLM Inference—0
PARAPHRASUS : A Comprehensive Benchmark for Evaluating Paraphrase Detection ModelsCode0
Decomposing Gaussians with Unknown CovarianceCode0
AALF: Almost Always Linear ForecastingCode0
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports—0
Model Selection Through Model Sorting—0
Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge TypesCode0
Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme—0
Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models—0
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