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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

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Showing 1501–1550 of 2050 papers

TitleStatusHype
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANsCode1
A New Compensatory Genetic Algorithm-Based Method for Effective Compressed Multi-function Convolutional Neural Network Model Selection with Multi-Objective Optimization—0
Variational Resampling Based Assessment of Deep Neural Networks under Distribution ShiftCode0
Using anomaly detection to support classification of fast running (packaging) processes—0
Estimating Real Log Canonical Thresholds—0
Off-Policy Evaluation via Off-Policy Classification—0
Model selection for contextual banditsCode0
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal InferenceCode0
Towards Accurate Model Selection in Deep Unsupervised Domain AdaptationCode0
Predicting Global Variations in Outdoor PM2.5 Concentrations using Satellite Images and Deep Convolutional Neural Networks—0
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter—0
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage—0
INFaaS: A Model-less and Managed Inference Serving SystemCode0
Unsupervised Model Selection for Variational Disentangled Representation Learning—0
Lifelong Bayesian Optimization—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
Automatic Model Selection for Neural Networks—0
Information criteria for non-normalized models—0
Decision Making with Machine Learning and ROC Curves—0
Disentangling Factors of Variation Using Few Labels—0
Interpretable multiclass classification by MDL-based rule listsCode1
Post-Selection Inference in Three-Dimensional Panel Data—0
On Learning to Prove—0
S^2-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning—0
Bayesian leave-one-out cross-validation for large data—0
BERTScore: Evaluating Text Generation with BERTCode1
A deep learning based solution for construction equipment detection: from development to deployment—0
Forecasting with time series imagingCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
Cramer-Rao Bound for Estimation After Model Selection and its Application to Sparse Vector Estimation—0
Variational Bayes for high-dimensional linear regression with sparse priors—0
Deep Learning Inversion of Electrical Resistivity Data—0
Bayesian Neural Networks at Finite TemperatureCode0
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph ClusteringCode0
Adaptive Sequential Machine Learning—0
Easy Transfer Learning By Exploiting Intra-domain Structures—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection—0
Combining Model and Parameter Uncertainty in Bayesian Neural NetworksCode0
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