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 501–525 of 2050 papers

TitleStatusHype
A Dirichlet stochastic block model for composition-weighted networks—0
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
FiCo-ITR: bridging fine-grained and coarse-grained image-text retrieval for comparative performance analysisCode0
AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video AnalyticsCode0
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
Navigating Uncertainty in Medical Image Segmentation—0
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and FairnessCode0
Patched RTC: evaluating LLMs for diverse software development tasksCode0
Zero-Shot Embeddings Inform Learning and Forgetting with Vision-Language Encoders—0
Modeling flexible behavior with remapping-based hippocampal sequence learning—0
Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes—0
Is F_1 Score Suboptimal for Cybersecurity Models? Introducing C_score, a Cost-Aware Alternative for Model Assessment—0
Realistic Evaluation of Test-Time Adaptation Algorithms: Unsupervised Hyperparameter Selection—0
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity ModelsCode0
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity—0
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence—0
Subject-driven Text-to-Image Generation via Preference-based Reinforcement LearningCode0
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation SystemsCode0
On Leakage of Code Generation Evaluation Datasets—0
Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function IdentificationsCode0
Comparative Analysis of LSTM Neural Networks and Traditional Machine Learning Models for Predicting Diabetes Patient Readmission—0
Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets—0
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender SystemsCode0
Greedy equivalence search for nonparametric graphical models—0
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