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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 1151–1175 of 2050 papers

TitleStatusHype
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
Model Selection for Time Series Forecasting: Empirical Analysis of Different EstimatorsCode0
Model Selection's Disparate Impact in Real-World Deep Learning Applications—0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
OTCE: A Transferability Metric for Cross-Domain Cross-Task RepresentationsCode1
A Powerful Subvector Anderson Rubin Test in Linear Instrumental Variables Regression with Conditional Heteroskedasticity—0
Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation—0
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
The Shape of Learning Curves: a ReviewCode0
Modeling the Second Player in Distributionally Robust OptimizationCode1
CNN Model & Tuning for Global Road Damage DetectionCode1
Deep Time Series Models for Scarce Data—0
Learning Word-Level Confidence For Subword End-to-End ASR—0
Reframing Neural Networks: Deep Structure in Overcomplete Representations—0
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies—0
Model Complexity of Deep Learning: A Survey—0
Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases—0
General Bayesian time-varying parameter VARs for predicting government bond yields—0
AutoAI-TS: AutoAI for Time Series Forecasting—0
Partially Hidden Markov Chain Linear Autoregressive model: inference and forecastingCode0
Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies—0
Two Sides of Meta-Learning Evaluation: In vs. Out of DistributionCode0
Bridging factor and sparse models—0
LogME: Practical Assessment of Pre-trained Models for Transfer LearningCode1
Parsimonious Modelling for Estimating Hospital Cooling Demand to Improve Energy Efficiency—0
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