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 1771–1780 of 2050 papers

TitleStatusHype
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message LengthCode0
DiTMoS: Delving into Diverse Tiny-Model Selection on MicrocontrollersCode0
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural CircuitsCode0
Predictive Analytics of Varieties of PotatoesCode0
Unsupervised Video Summarization via Iterative Training and Simplified GANCode0
Graphical posterior predictive classifier: Bayesian model averaging with particle GibbsCode0
Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series ClassificationCode0
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts modelsCode0
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender SystemsCode0
Model selection with lasso-zero: adding straw to the haystack to better find needlesCode0
Show:102550
← PrevPage 178 of 205Next →

No leaderboard results yet.