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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 901–910 of 2050 papers

TitleStatusHype
Unsupervised Image Representation Learning with Deep Latent ParticlesCode1
Understanding new tasks through the lens of training data via exponential tiltingCode0
Verifying Learning-Based Robotic Navigation Systems—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly DetectionCode5
Fusion Subspace Clustering for Incomplete Data—0
Fast Instrument Learning with Faster RatesCode0
Time Series Anomaly Detection via Reinforcement Learning-Based Model SelectionCode1
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
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