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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 501–510 of 2050 papers

TitleStatusHype
A Dirichlet stochastic block model for composition-weighted networks—0
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
FiCo-ITR: bridging fine-grained and coarse-grained image-text retrieval for comparative performance analysisCode0
AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video AnalyticsCode0
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and FairnessCode0
Patched RTC: evaluating LLMs for diverse software development tasksCode0
Navigating Uncertainty in Medical Image Segmentation—0
Zero-Shot Embeddings Inform Learning and Forgetting with Vision-Language Encoders—0
Modeling flexible behavior with remapping-based hippocampal sequence learning—0
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