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 1041–1050 of 2050 papers

TitleStatusHype
Towards trustworthy explanations with gradient-based attribution methods—0
Towards Typologically Aware Rescoring to Mitigate Unfaithfulness in Lower-Resource Languages—0
Towards Unsupervised Validation of Anomaly-Detection Models—0
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
Training Deep Neural Networks for Wireless Sensor Networks Using Loosely and Weakly Labeled Images—0
Training Machine Learning Models to Characterize Temporal Evolution of Disadvantaged Communities—0
Train on Validation: Squeezing the Data Lemon—0
Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction—0
Transformers4NewsRec: A Transformer-based News Recommendation Framework—0
Bayesian Image Classification with Deep Convolutional Gaussian Processes—0
Show:102550
← PrevPage 105 of 205Next →

No leaderboard results yet.