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Benchmarking

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

https://github.com/bigscience-workshop/biomedical

Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

https://github.com/bioscan-ml/bioscan-5m

An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders

https://github.com/bkmi/cnre

Benchmarking Simulation-Based Inference

https://github.com/bmw-group-quantum/quark

Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK

https://github.com/borisdayma/clip-jax

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/bradyfu/awesome-multimodal-large-language-models

MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

https://github.com/bragancas/vnet_pytorch-atriaseg2018

A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging

https://github.com/brandontrabucco/cider

CIDEr: Consensus-based Image Description Evaluation

https://github.com/bravegroup/sheetcopilot

SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models

https://github.com/brown-palm/ObjectPrompt

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/brucewlee/lftk

Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach

https://github.com/bruthyu/bpt-vlm

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/bspanjers/AIFBAREND

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

https://github.com/bstadie/third_person_im

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/buyeah1109/KEN

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/buyeah1109/finc

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/c3210927/point_cnn

PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding

https://github.com/caihuaiguang/CHG-Shapley-for-Data-Valuation

OpenDataVal: a Unified Benchmark for Data Valuation

https://github.com/calderkatyal/CPSC483FinalProject

Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks

https://github.com/camma-public/mcit-ig

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

https://github.com/camma-public/rendezvous

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

https://github.com/camma-public/rendezvous-in-time

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

https://github.com/carla-recourse/CARLA

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

https://github.com/castorini/nuggetizer

Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track

https://github.com/cataai/posex

State-specific protein-ligand complex structure prediction with a multi-scale deep generative model

https://github.com/cathywu/rllab-multiagent

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/cbfinn/maml_rl

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/cch1999/posecheck

Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?

https://github.com/cdpierse/transformers-interpret

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

https://github.com/cesarojuliana/feature_importance_fairness

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

https://github.com/changminwu/expandergnn

Benchmarking Graph Neural Networks

https://github.com/chenfengzhao/gnnhls

Benchmarking Graph Neural Networks

https://github.com/chenjiyan2001/paddle-FGCNN

BARS-CTR: Open Benchmarking for Click-Through Rate Prediction

https://github.com/chenzhenpeng18/biasmitigationstudy

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

https://github.com/chenzhenpeng18/fse22-maat

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

https://github.com/chenzhenpeng18/icse24-multi-attribute-fairness

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

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