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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 1026–1050 of 2050 papers

TitleStatusHype
Towards a Theoretical Framework of Out-of-Distribution Generalization—0
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools—0
Towards Better Citation Intent Classification—0
Towards Deep Learning-aided Wireless Channel Estimation and Channel State Information Feedback for 6G—0
Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques—0
Towards Fundamentally Scalable Model Selection: Asymptotically Fast Update and Selection—0
Towards Improved Variational Inference for Deep Bayesian Models—0
Towards more transferable adversarial attack in black-box manner—0
Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management—0
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution—0
Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Framework—0
On the Limitation and Experience Replay for GNNs in Continual Learning—0
Towards Safer Online Spaces: Simulating and Assessing Intervention Strategies for Eating Disorder Discussions—0
Towards Stable and Comprehensive Domain Alignment: Max-Margin Domain-Adversarial Training—0
Towards Transferable Adversarial Perturbations with Minimum Norm—0
Towards trustworthy explanations with gradient-based attribution methods—0
Towards Typologically Aware Rescoring to Mitigate Unfaithfulness in Lower-Resource Languages—0
Towards Unsupervised Validation of Anomaly-Detection Models—0
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
Training Deep Neural Networks for Wireless Sensor Networks Using Loosely and Weakly Labeled Images—0
Training Machine Learning Models to Characterize Temporal Evolution of Disadvantaged Communities—0
Train on Validation: Squeezing the Data Lemon—0
Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction—0
Transformers4NewsRec: A Transformer-based News Recommendation Framework—0
Bayesian Image Classification with Deep Convolutional Gaussian Processes—0
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