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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 321–330 of 2050 papers

TitleStatusHype
Multi-model Stochastic Particle-based Variational Bayesian Inference for Multiband Delay Estimation—0
Understanding the Limits of Deep Tabular Methods with Temporal Shift—0
Forecasting Whole-Brain Neuronal Activity from Volumetric Video—0
Validating the predictions of mathematical models describing tumor growth and treatment response—0
Extremely Greedy Equivalence SearchCode0
Independent Mobility GPT (IDM-GPT): A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Models—0
Capability Instruction Tuning: A New Paradigm for Dynamic LLM RoutingCode0
Towards Typologically Aware Rescoring to Mitigate Unfaithfulness in Lower-Resource Languages—0
Subsampling Graphs with GNN Performance Guarantees—0
Network Resource Optimization for ML-Based UAV Condition Monitoring with Vibration Analysis—0
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