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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 1331–1340 of 2050 papers

TitleStatusHype
Tuning In: Analysis of Audio Classifier Performance in Clinical Settings with Limited Data—0
Tuning Large language model for End-to-end Speech Translation—0
Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System—0
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition—0
Two-level histograms for dealing with outliers and heavy tail distributions—0
Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data—0
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust—0
Uncertainty Profiles for LLMs: Uncertainty Source Decomposition and Adaptive Model-Metric Selection—0
Understanding and Estimating the Adaptability of Domain-Invariant Representations—0
Understanding prompt engineering may not require rethinking generalization—0
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