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 551–560 of 2050 papers

TitleStatusHype
IW-GAE: Importance Weighted Group Accuracy Estimation for Improved Calibration and Model Selection in Unsupervised Domain Adaptation—0
RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language ModelsCode1
One For All & All For One: Bypassing Hyperparameter Tuning with Model Averaging For Cross-Lingual TransferCode0
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data—0
Target Variable Engineering—0
To token or not to token: A Comparative Study of Text Representations for Cross-Lingual TransferCode0
Towards Robust Multi-Modal Reasoning via Model SelectionCode1
On the Computational Complexity of Private High-dimensional Model SelectionCode0
Risk Aware Benchmarking of Large Language Models—0
Transformers for Green Semantic Communication: Less Energy, More SemanticsCode0
Show:102550
← PrevPage 56 of 205Next →

No leaderboard results yet.