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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/MindCode-4/code-8/tree/main/OTCE

OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations

https://github.com/MindSpore-scientific/code-5/tree/main/OTCE

OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations

https://github.com/ModelOriented/DrWhy

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

https://github.com/ModelOriented/EloML

EPP: interpretable score of model predictive power

https://github.com/Neuraxio/Neuraxle

Scikit-learn: Machine Learning in Python

https://github.com/PlaytikaOSS/pybandits

Doubly robust off-policy evaluation with shrinkage

https://github.com/Pongpisit-Thanasutives/CHS

Improved identification accuracy in equation learning via comprehensive $\boldsymbol{R^2}$-elimination and Bayesian model selection

https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection

Large Scale Correlation Clustering Optimization

https://github.com/Ruhallah93/superior-scoring-rules

Superior Scoring Rules for Probabilistic Evaluation of Single-Label Multi-Class Classification Tasks

https://github.com/RyanLucas3/Adaptive-Time-Series-Learning

DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluation

https://github.com/SalmanMohammadi/msci-fairnessdisentanglement

Odd-One-Out Representation Learning

https://github.com/ScandEval/ScandEval

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

https://github.com/ShiYaya/Awesome_Evaluation_Metrics_for_Text_Generation

BERTScore: Evaluating Text Generation with BERT

https://github.com/SirRob1997/Collaborative-Filtering-under-Model-Uncertainty

Predictive Multiplicity in Classification

https://github.com/SirRob1997/collaborative_filtering

Predictive Multiplicity in Classification

https://github.com/Soldie/stux-DNN

Learning Sparse Neural Networks through $L_0$ Regularization

https://github.com/THUDM/KOBE

BERTScore: Evaluating Text Generation with BERT

https://github.com/THUcqb/KOBE

BERTScore: Evaluating Text Generation with BERT

https://github.com/YaohuiZeng/biglasso

Hybrid safe-strong rules for efficient optimization in lasso-type problems

https://github.com/YipengUva/RpESCA

Separating common (global and local) and distinct variation in multiple mixed types data sets

https://github.com/YipengUva/pESCA_matlab

Separating common (global and local) and distinct variation in multiple mixed types data sets

https://github.com/YugeTen/fish

In Search of Lost Domain Generalization

https://github.com/abess-team/abess

abess: A Fast Best Subset Selection Library in Python and R

https://github.com/agrija9/deep-unsupervised-domain-adaptation

Deep Domain Confusion: Maximizing for Domain Invariance

https://github.com/alan-turing-institute/bocpdms

Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection

https://github.com/aliaksah/variational-inference-for-bayesian-neural-networks-under-model-and-parameter-uncertainty

Combining Model and Parameter Uncertainty in Bayesian Neural Networks

https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/model/dbmtl.py

Deep Bayesian Multi-Target Learning for Recommender Systems

https://github.com/alibaba/TorchEasyRec

Deep Bayesian Multi-Target Learning for Recommender Systems

https://github.com/all-umass/metric_learn

metric-learn: Metric Learning Algorithms in Python

https://github.com/allenai/mslr-shared-task

BERTScore: Evaluating Text Generation with BERT

https://github.com/ananyacoder/mee_wmt2021

BERTScore: Evaluating Text Generation with BERT

https://github.com/andreeaiana/newsreclib

Peeling Back the Layers: An In-Depth Evaluation of Encoder Architectures in Neural News Recommenders

https://github.com/andychinka/dcase-challenge

Tune: A Research Platform for Distributed Model Selection and Training

https://github.com/anissa218/medfair_ukbb

MEDFAIR: Benchmarking Fairness for Medical Imaging

https://github.com/anna-neufeld/countsplit

Data thinning for convolution-closed distributions

https://github.com/anthonysicilia/change-that-matters-acl2022

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

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