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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 321–330 of 2050 papers

TitleStatusHype
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
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction TasksCode1
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
Superior Scoring Rules for Probabilistic Evaluation of Single-Label Multi-Class Classification TasksCode1
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and FairnessCode0
Navigating Uncertainty in Medical Image Segmentation—0
Patched RTC: evaluating LLMs for diverse software development tasksCode0
Zero-Shot Embeddings Inform Learning and Forgetting with Vision-Language Encoders—0
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