Papers with Code 아카이브

Model Selection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

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/HMProenca/RuleList

공식 코드

Interpretable multiclass classification by MDL-based rule lists

https://github.com/HannaMeyer/CAST

공식 코드

The CAST package for training and assessment of spatial prediction models in R

https://github.com/Healthy-AI/IncomeSCM

공식 코드

IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark

https://github.com/IBM/geostats-gen-error

공식 코드

Geostatistical Learning: Challenges and Opportunities

https://github.com/IntelLabs/causality-lab

공식 코드

Bayesian Structure Learning by Recursive Bootstrap

https://github.com/JereKoskela/Beta-Xi-Sim

공식 코드

Multi-locus data distinguishes between population growth and multiple merger coalescents

https://github.com/Jianbo-Lab/DAGGER

공식 코드

DAGGER: A sequential algorithm for FDR control on DAGs

https://github.com/JieGroup/On-Statistical-Efficiency-in-Learning

공식 코드

On Statistical Efficiency in Learning

https://github.com/LCSL/NYTRO

공식 코드

NYTRO: When Subsampling Meets Early Stopping

https://github.com/LINs-lab/M3

공식 코드

Towards Robust Multi-Modal Reasoning via Model Selection

https://github.com/LLMSELECTOR/LLMSELECTOR

공식 코드

Optimizing Model Selection for Compound AI Systems

https://github.com/LourensT/DynamicNetworkSimulation

공식 코드

Learning the mechanisms of network growth

https://github.com/M3RG-IITD/llamat

공식 코드

Foundational Large Language Models for Materials Research

https://github.com/MLD3/OfflineRL_ModelSelection

공식 코드

Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings

https://github.com/MedChaabane/deepRAM

공식 코드

Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding Specificities

https://github.com/MeherBhaskar/OptiMindTune

공식 코드

OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization

https://github.com/Monoxido45/PhyloHclust

공식 코드

Hierarchical clustering: visualization, feature importance and model selection

https://github.com/Mr8ND/cdetools_applications

공식 코드

Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference

https://github.com/NJU-LHRS/LHRS-Bot

공식 코드

LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language Interpretation

https://github.com/PKU-DAIR/mindware

공식 코드

VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition

https://github.com/PV-Lab/Benchmarking

공식 코드

Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

https://github.com/RangamaniLabUCSD/multimodel-inference

공식 코드

Increasing certainty in systems biology models using Bayesian multimodel inference

https://github.com/RashidBakirov/multiple-adaptive-mechanisms

공식 코드

Automated Adaptation Strategies for Stream Learning

https://github.com/RuiZhang2016/Variational-Inference-for-SGP-Modulated-Hawkes-Process

공식 코드

Variational Inference for Sparse Gaussian Process Modulated Hawkes Process

https://github.com/STAC-USC/DeepNNK_polytope_interpolation

공식 코드

DeepNNK: Explaining deep models and their generalization using polytope interpolation

https://github.com/SoumyaTGhosh/hs-bnn-public

공식 코드

Model Selection in Bayesian Neural Networks via Horseshoe Priors

https://github.com/SuryanarayanaMK/PDE-STRIDE

공식 코드

Stability selection enables robust learning of partial differential equations from limited noisy data

https://github.com/TammoR/LogicalFactorisationMachines

공식 코드

TensOrMachine: Probabilistic Boolean Tensor Decomposition

https://github.com/ThomasBrouwer/BNMTF_ARD

공식 코드

Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

https://github.com/Tiiiger/bert_score

공식 코드

BERTScore: Evaluating Text Generation with BERT

https://github.com/Trung-TinNGUYEN/NamsGLoME-Simulation

공식 코드

A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models

https://github.com/Vadori/CytoArk

공식 코드

Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and Classification

https://github.com/Vaibhavs10/anli-performance-prediction

공식 코드

Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference

https://github.com/XiangLi1999/AutoBencher

공식 코드

AutoBencher: Creating Salient, Novel, Difficult Datasets for Language Models

https://github.com/YaroslavAveryanov/Minimum-discrepancy-principle-for-choosing-k

공식 코드

Minimum discrepancy principle strategy for choosing $k$ in $k$-NN regression

https://github.com/YecanLee/2BeOETG

공식 코드

Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework

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