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 1526–1550 of 2050 papers

TitleStatusHype
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter—0
INFaaS: A Model-less and Managed Inference Serving SystemCode0
Lifelong Bayesian Optimization—0
Unsupervised Model Selection for Variational Disentangled Representation Learning—0
Deep Generalized Method of Moments for Instrumental Variable AnalysisCode0
A Geometric Modeling of Occam's Razor in Deep Learning—0
Cold Case: The Lost MNIST DigitsCode0
Variational Inference for Sparse Gaussian Process Modulated Hawkes ProcessCode0
Model Validation Using Mutated Training Labels: An Exploratory Study—0
Distributionally Robust Formulation and Model Selection for the Graphical LassoCode0
Adaptive Model Selection Framework: An Application to Airline Pricing—0
Catastrophic forgetting: still a problem for DNNsCode0
Analysis of the AutoML Challenge Series 2015–2018—0
Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood MaximizationCode0
Gmail Smart Compose: Real-Time Assisted Writing—0
Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression—0
Information criteria for non-normalized models—0
Automatic Model Selection for Neural Networks—0
Decision Making with Machine Learning and ROC Curves—0
Disentangling Factors of Variation Using Few Labels—0
Post-Selection Inference in Three-Dimensional Panel Data—0
S^2-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning—0
On Learning to Prove—0
Bayesian leave-one-out cross-validation for large data—0
A deep learning based solution for construction equipment detection: from development to deployment—0
Show:102550
← PrevPage 62 of 82Next →

No leaderboard results yet.