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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 31–40 of 2050 papers

TitleStatusHype
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster ManagementCode1
LENSLLM: Unveiling Fine-Tuning Dynamics for LLM SelectionCode1
Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and ClassificationCode1
Time Series Embedding Methods for Classification Tasks: A ReviewCode1
Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing dataCode1
Towards Unsupervised Model Selection for Domain Adaptive Object DetectionCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
NLP-ADBench: NLP Anomaly Detection BenchmarkCode1
Evaluating Language Models as Synthetic Data GeneratorsCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
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