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 831–840 of 2050 papers

TitleStatusHype
Feature-based model selection for object detection from point cloud data—0
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
Partial sequence labeling with structured Gaussian Processes—0
De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs—0
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning PerspectiveCode2
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
clusterBMA: Bayesian model averaging for clusteringCode1
Towards Deep Learning-aided Wireless Channel Estimation and Channel State Information Feedback for 6G—0
Model Selection in High-Dimensional Block-Sparse Linear Regression—0
Show:102550
← PrevPage 84 of 205Next →

No leaderboard results yet.