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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 1051–1100 of 2050 papers

TitleStatusHype
Trinary Tools for Continuously Valued Binary Classifiers—0
TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection—0
Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services—0
Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals—0
Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models—0
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization—0
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
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models—0
Understanding the double descent curve in Machine Learning—0
Understanding the Limits of Deep Tabular Methods with Temporal Shift—0
Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review—0
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction—0
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning—0
UniTN End-to-End Discourse Parser for CoNLL 2016 Shared Task—0
Universal and data-adaptive algorithms for model selection in linear contextual bandits—0
Universal Approximation of Edge Density in Large Graphs—0
Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks—0
Universum Learning for Multiclass SVM—0
Un modèle Bayésien de co-clustering de données mixtes—0
Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery—0
Unreflected Use of Tabular Data Repositories Can Undermine Research Quality—0
Unsupervised learning of regression mixture models with unknown number of components—0
Unsupervised Model Drift Estimation with Batch Normalization Statistics for Dataset Shift Detection and Model Selection—0
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance—0
Upper Confidence Bounds for Combining Stochastic Bandits—0
UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration—0
Use Of Vapnik-Chervonenkis Dimension in Model Selection—0
Using anomaly detection to support classification of fast running (packaging) processes—0
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies—0
Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications—0
Using Kernel Methods and Model Selection for Prediction of Preterm Birth—0
Utilizing AI Language Models to Identify Prognostic Factors for Coronary Artery Disease: A Study in Mashhad Residents—0
U-Trustworthy Models.Reliability, Competence, and Confidence in Decision-Making—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Validate on Sim, Detect on Real -- Model Selection for Domain Randomization—0
Validating the predictions of mathematical models describing tumor growth and treatment response—0
Variance function estimation in regression model via aggregation procedures—0
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction—0
Variational approach for learning Markov processes from time series data—0
Variational Bayes for high-dimensional linear regression with sparse priors—0
Variational Inference and Learning of Piecewise-linear Dynamical Systems—0
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