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 1676–1700 of 2050 papers

TitleStatusHype
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing—0
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis—0
Carbon Intensity-Aware Adaptive Inference of DNNs—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
Cats & Co: Categorical Time Series Coclustering—0
Causal Covariate Shift Correction using Fisher information penalty—0
Causal Discovery in Hawkes Processes by Minimum Description Length—0
Causal Falling Rule Lists—0
Causal Q-Aggregation for CATE Model Selection—0
Choice modelling in the age of machine learning - discussion paper—0
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI—0
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion—0
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels—0
Classification with Scattering Operators—0
Classification with Sparse Overlapping Groups—0
client2vec: Towards Systematic Baselines for Banking Applications—0
Clipper: A Low-Latency Online Prediction Serving System—0
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization—0
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
Clustering-Based Validation Splits for Model Selection under Domain Shift—0
Clustering Discrete-Valued Time Series—0
Clustering evolving data using kernel-based methods—0
Show:102550
← PrevPage 68 of 82Next →

No leaderboard results yet.