SOTAVerified

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 1191–1200 of 2050 papers

TitleStatusHype
Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare—0
Smart Multi-Modal Search: Contextual Sparse and Dense Embedding Integration in Adobe Express—0
Smooth Bandit Optimization: Generalization to Hölder Space—0
Solar Power Prediction Using Machine Learning—0
SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks—0
SPAgent: Adaptive Task Decomposition and Model Selection for General Video Generation and Editing—0
Sparse Estimation with Structured Dictionaries—0
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient—0
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?—0
Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection—0
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