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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 1051–1075 of 2050 papers

TitleStatusHype
Multi-Objective Model Selection for Time Series Forecasting—0
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments—0
Fitting Sparse Markov Models to Categorical Time Series Using Regularization—0
Loss-guided Stability Selection—0
Dependence model assessment and selection with DecoupleNets—0
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series—0
Discovering Distribution Shifts using Latent Space RepresentationsCode0
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing—0
JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion—0
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study—0
Learning Curves for Decision Making in Supervised Machine Learning: A Survey—0
The Time-Varying Multivariate Autoregressive Index Model—0
Evaluation of HTR models without Ground Truth MaterialCode0
One Step Is Enough for Few-Shot Cross-Lingual Transfer: Co-Training with Gradient Optimization—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics—0
Problem-dependent attention and effort in neural networks with applications to image resolution and model selection—0
Self-directed Machine Learning—0
Have I done enough planning or should I plan more?Code0
Deep Learning and Linear Programming for Automated Ensemble Forecasting and InterpretationCode0
Propagation Regularizer for Semi-Supervised Learning With Extremely Scarce Labeled Samples—0
A general technique for the estimation of farm animal body part weights from CT scans and its applications in a rabbit breeding programCode0
Optimal Model Averaging of Support Vector Machines in Diverging Model Spaces—0
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation—0
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations—0
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