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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 961–970 of 2050 papers

TitleStatusHype
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
Invariance Learning in Deep Neural Networks with Differentiable Laplace ApproximationsCode1
Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks—0
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold RegressionCode0
Multi-Objective Model Selection for Time Series Forecasting—0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments—0
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