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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/zju-vipa/DEPARA

공식 코드

DEPARA: Deep Attribution Graph for Deep Knowledge Transferability

https://github.com/zsk66/flame

공식 코드

On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness

https://gitlab.com/claudius-krause/caloflow

공식 코드

CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows

https://github.com/AKASH2907/Forgery-Classification-via-Domain-Adaptation

Deep Domain Confusion: Maximizing for Domain Invariance

https://github.com/AlexHeyman/PopulationBasedTraining

Population Based Training of Neural Networks

https://github.com/AmeerHajAli/ray2

Tune: A Research Platform for Distributed Model Selection and Training

https://github.com/Anish144/causal_discovery_bayesian_model_selection

Bivariate Causal Discovery using Bayesian Model Selection

https://github.com/BPfuelb/CF_in_DNNs

Catastrophic forgetting: still a problem for DNNs

https://github.com/CAMMA-public/cholect50

Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets

https://github.com/CtrlZ1/Domain-Adaptation-Algorithms

Deep Domain Confusion: Maximizing for Domain Invariance

https://github.com/CtrlZ1/Domain-Adaptive-CodeBase

Deep Domain Confusion: Maximizing for Domain Invariance

https://github.com/D3-AI/Cardea

Cardea: An Open Automated Machine Learning Framework for Electronic Health Records

https://github.com/DataCanvasIO/Hypernets

Tune: A Research Platform for Distributed Model Selection and Training

https://github.com/DominikaDu/QSmooth

Scikit-learn: Machine Learning in Python

https://github.com/ENSTA-U2IS-AI/torch-uncertainty

Laplace Redux -- Effortless Bayesian Deep Learning

https://github.com/EduardoTrevino/fail-talms

Evaluating Language Models as Synthetic Data Generators

https://github.com/EverFAITH/NAIM3L

A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label Learning

https://github.com/GenDisc/IVR

Evaluating Weakly Supervised Object Localization Methods Right

https://github.com/HMProenca/RobustSubgroupDiscovery

Interpretable multiclass classification by MDL-based rule lists

https://github.com/HMProenca/robust-rules-for-prediction-and-description

Interpretable multiclass classification by MDL-based rule lists

https://github.com/HardikPrabhu/Energy-Time-series-anomaly-detection

DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density Estimation

https://github.com/HazeDT/WaveletKernelNet

Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study

https://github.com/IBM/causallib

An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference

https://github.com/Ibotta/mr_uplift

A comparison of methods for model selection when estimating individual treatment effects

https://github.com/JoeOsborn/mechlearn

CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis

https://github.com/JonasLeininger/ray-population-based-training

Population Based Training of Neural Networks

https://github.com/KDiazOrdaz/Machine-learning-in-policy-evaluation-new-tools-for-causal-inference

Machine learning in policy evaluation: new tools for causal inference

https://github.com/Koukyosyumei/AIJack

Evasion Attacks against Machine Learning at Test Time

https://github.com/LGE-ARC-AdvancedAI/MU-SVM

Multiclass Universum SVM

https://github.com/Larsvanderlaan/causalGLM

Assumption-lean inference for generalised linear model parameters

https://github.com/MI2DataLab/factorMerger

The Merging Path Plot: adaptive fusing of k-groups with likelihood-based model selection

https://github.com/MS-Mind/MS-Code-08/tree/main/metric_learn

metric-learn: Metric Learning Algorithms in Python

https://github.com/MS-P3/code7/tree/main/vision_text_dual_encoder

Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU Tasks

https://github.com/MattKleinsmith/pbt

Population Based Training of Neural Networks

https://github.com/Mind23-2/MindCode-101/tree/main/metric_learn

metric-learn: Metric Learning Algorithms in Python

https://github.com/Mind23-2/MindCode-3/tree/main/metric_learn

metric-learn: Metric Learning Algorithms in Python

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.