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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 271–280 of 2050 papers

TitleStatusHype
Multimodal Benchmarking and Recommendation of Text-to-Image Generation ModelsCode0
Knowledge Guided Encoder-Decoder Framework: Integrating Multiple Physical Models for Agricultural Ecosystem Modeling—0
Mallows-type model averaging: Non-asymptotic analysis and all-subset combination—0
ReeM: Ensemble Building Thermodynamics Model for Efficient HVAC Control via Hierarchical Reinforcement Learning—0
Logits-Constrained Framework with RoBERTa for Ancient Chinese NER—0
A Practitioner's Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications—0
Generative diffusion model surrogates for mechanistic agent-based biological models—0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Framework—0
Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare—0
Show:102550
← PrevPage 28 of 205Next →

No leaderboard results yet.