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 1091–1100 of 2050 papers

TitleStatusHype
Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior—0
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
Machine Learning-Assisted Analysis of Small Angle X-ray Scattering—0
Towards Better Citation Intent Classification—0
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning—0
Optimizing Unlicensed Coexistence Network Performance Through Data Learning—0
A Rule-Based Epidemiological Modelling Framework—0
Guided Sampling-based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis—0
Universal and data-adaptive algorithms for model selection in linear contextual bandits—0
Joint Inference for Neural Network Depth and Dropout RegularizationCode0
Show:102550
← PrevPage 110 of 205Next →

No leaderboard results yet.