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 761–770 of 2050 papers

TitleStatusHype
Predictive Modeling through Hyper-Bayesian Optimization—0
A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI—0
An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading—0
Learning Disentangled Discrete RepresentationsCode0
Rational kernel-based interpolation for complex-valued frequency response functions—0
Consistent model selection in the spiked Wigner model via AIC-type criteria—0
Anytime Model Selection in Linear BanditsCode0
Adaptive debiased machine learning using data-driven model selection techniques—0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Towards a performance analysis on pre-trained Visual Question Answering models for autonomous drivingCode0
Show:102550
← PrevPage 77 of 205Next →

No leaderboard results yet.