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 201–210 of 2050 papers

TitleStatusHype
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark StudyCode1
Deep learning for dynamic graphs: models and benchmarksCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
DEPARA: Deep Attribution Graph for Deep Knowledge TransferabilityCode1
LogME: Practical Assessment of Pre-trained Models for Transfer LearningCode1
Assumption-lean inference for generalised linear model parametersCode1
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy LabelsCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing dataCode1
You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly DetectionCode1
Show:102550
← PrevPage 21 of 205Next →

No leaderboard results yet.