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 376–400 of 2050 papers

TitleStatusHype
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free RegularizationCode0
Capability Instruction Tuning: A New Paradigm for Dynamic LLM RoutingCode0
ARDA: Automatic Relational Data Augmentation for Machine LearningCode0
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning AlgorithmsCode0
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts modelsCode0
A Deep Learning Method for Comparing Bayesian Hierarchical ModelsCode0
Evaluation of HTR models without Ground Truth MaterialCode0
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmarkCode0
Catastrophic forgetting: still a problem for DNNsCode0
Individualized Prediction of COVID-19 Adverse outcomes with MLHOCode0
Reliable Time Prediction in the Markov Stochastic Block ModelCode0
Inferring Convolutional Neural Networks' accuracies from their architectural characterizationsCode0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
Exploring Model Transferability through the Lens of Potential EnergyCode0
Best Arm Identification for Stochastic Rising BanditsCode0
Model selection for contextual banditsCode0
An Offline Metric for the Debiasedness of Click ModelsCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical TextsCode0
Improved Group Robustness via Classifier Retraining on Independent SplitsCode0
Show:102550
← PrevPage 16 of 82Next →

No leaderboard results yet.