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

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Showing 101–125 of 2050 papers

TitleStatusHype
Nearest Neighbour Equilibrium ClusteringCode0
Advancements in Natural Language Processing: Exploring Transformer-Based Architectures for Text Understanding—0
Face Spoofing Detection using Deep LearningCode0
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
Patch-based learning of adaptive Total Variation parameter maps for blind image denoising—0
Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks—0
Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal PredictionCode0
Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing DataCode0
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly DetectionCode0
End-to-End Edge AI Service Provisioning Framework in 6G ORAN—0
Unreflected Use of Tabular Data Repositories Can Undermine Research Quality—0
ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks—0
Conformal Prediction with Upper and Lower Bound Models—0
Network Traffic Classification Using Machine Learning, Transformer, and Large Language Models—0
Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR—0
Ranking pre-trained segmentation models for zero-shot transferability—0
BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge—0
Multi-model Stochastic Particle-based Variational Bayesian Inference for Multiband Delay Estimation—0
Forecasting Whole-Brain Neuronal Activity from Volumetric Video—0
Understanding the Limits of Deep Tabular Methods with Temporal Shift—0
Extremely Greedy Equivalence SearchCode0
FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series ForecastingCode2
Validating the predictions of mathematical models describing tumor growth and treatment response—0
Independent Mobility GPT (IDM-GPT): A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Models—0
Towards Typologically Aware Rescoring to Mitigate Unfaithfulness in Lower-Resource Languages—0
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