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Model Selection

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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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/andrade-stats/convexCovariateClusteringClassification

공식 코드

Convex Covariate Clustering for Classification

https://github.com/andrade-stats/robustBayesClustering

공식 코드

Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model

https://github.com/andrew-miao/RPO

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Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning

https://github.com/andrewktan/pareto_dib

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Pareto-optimal clustering with the primal deterministic information bottleneck

https://github.com/angrymaciek/warlock

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Warlock: an automated computational workflow for simulating spatially structured tumour evolution

https://github.com/anjalisilva/mixmplnfa

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Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count Data

https://github.com/anthonysicilia/leather-aacl2022

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LEATHER: A Framework for Learning to Generate Human-like Text in Dialogue

https://github.com/anyatarascina/pyrvtest

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Testing Firm Conduct

https://github.com/apmoore1/fiesta

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FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms

https://github.com/aramis-lab/AD-DL

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Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation

https://github.com/areeg94fahad/yolov11

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YOLOv11 Optimization for Efficient Resource Utilization

https://github.com/ars22/meta-learning-eval-id-vs-ood

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Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution

https://github.com/astro-informatics/harmonic

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Learned harmonic mean estimation of the marginal likelihood with normalizing flows

https://github.com/astro-informatics/proxnest

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Proximal nested sampling with data-driven priors for physical scientists

https://github.com/aswilson07/ApproximateCV

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Approximate Cross-validation: Guarantees for Model Assessment and Selection

https://github.com/atcemgil/bam

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Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya Urns

https://github.com/athenarc/citationintentopenllm

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Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMs

https://github.com/atraxus/ha-es

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Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost

https://github.com/atschalz/dc_tabeval

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A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data

https://github.com/atutej/token-language-adaptation

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Exploring Design Choices for Building Language-Specific LLMs

https://github.com/automl/dontwasteyourtime-early-stopping

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Don't Waste Your Time: Early Stopping Cross-Validation

https://github.com/autonlab/aqua

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AQuA: A Benchmarking Tool for Label Quality Assessment

https://github.com/awindmann/cps-robustness-benchmark

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Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems

https://github.com/axa-rev-research/comparing-fairml-strategies

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When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairness

https://github.com/ba1jun/model-selection-airtran

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Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking

https://github.com/baidu/Familia

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Familia: An Open-Source Toolkit for Industrial Topic Modeling

https://github.com/bayesflow-org/hierarchical-model-comparison

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A Deep Learning Method for Comparing Bayesian Hierarchical Models

https://github.com/bgu-cs-vil/deepdpm

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DeepDPM: Deep Clustering With an Unknown Number of Clusters

https://github.com/bigml-cs-ucla/spuco

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Challenges and Opportunities in Improving Worst-Group Generalization in Presence of Spurious Features

https://github.com/biomathlab/PDE-Learning-few-time-samples

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Learning Equations from Biological Data with Limited Time Samples

https://github.com/biomedia-mbzuai/dgm-dr

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DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification

https://github.com/bioscan-ml/barcodebert

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BarcodeBERT: Transformers for Biodiversity Analysis

https://github.com/bmda-unibas/condinvariancecc

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Learning Conditional Invariance through Cycle Consistency

https://github.com/boguss1225/ObjectDetectionGUI

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TensorFlow with user friendly Graphical Framework for object detection API

https://github.com/boschresearch/bosot

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Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport

https://github.com/boschresearch/f1-is-not-enough

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F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering

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