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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 181–190 of 2050 papers

TitleStatusHype
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective—0
MODfinity: Unsupervised Domain Adaptation with Multimodal Information Flow Intertwining—0
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks—0
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework—0
Adversarial Negotiation Dynamics in Generative Language Models—0
Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing dataCode1
Recommending Pre-Trained Models for IoT Devices—0
Structure Learning in Gaussian Graphical Models from Glauber Dynamics—0
Exploring Dynamic Novel View Synthesis Technologies for Cinematography—0
Towards Unsupervised Model Selection for Domain Adaptive Object DetectionCode1
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