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 291–300 of 2050 papers

TitleStatusHype
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems—0
LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot SystemsCode0
MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications—0
Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!—0
Triple equivalence for the emergence of biological intelligenceCode1
Towards Safer Online Spaces: Simulating and Assessing Intervention Strategies for Eating Disorder Discussions—0
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion DetectionCode1
On the effectiveness of smartphone IMU sensors and Deep Learning in the detection of cardiorespiratory conditions—0
On the Effects of Modeling on the Sim-to-Real Transfer Gap in Twinning the POWDER Platform—0
Show:102550
← PrevPage 30 of 205Next →

No leaderboard results yet.