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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 1876–1900 of 2050 papers

TitleStatusHype
Inferring Convolutional Neural Networks' accuracies from their architectural characterizationsCode0
Framework for Inferring Following Strategies from Time Series of Movement DataCode0
Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing DataCode0
Combining UPerNet and ConvNeXt for Contrails Identification to reduce Global WarmingCode0
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient EvaluationCode0
Diagnostic Tool for Out-of-Sample Model EvaluationCode0
Automated Dependence PlotsCode0
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph ClusteringCode0
A general technique for the estimation of farm animal body part weights from CT scans and its applications in a rabbit breeding programCode0
Quality Estimation for Image Captions Based on Large-scale Human EvaluationsCode0
Towards Model Selection using Learning Curve Cross-ValidationCode0
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect EstimationCode0
DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy ClassificationCode0
Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation SystemsCode0
Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical SystemsCode0
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)Code0
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model LeaderboardsCode0
Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and QuantificationCode0
Understanding new tasks through the lens of training data via exponential tiltingCode0
ARDA: Automatic Relational Data Augmentation for Machine LearningCode0
Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free RegularizationCode0
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM PerformanceCode0
AALF: Almost Always Linear ForecastingCode0
Degrees of Freedom and Model Selection for k-means ClusteringCode0
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