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

https://github.com/snel-repo/expressive-latent-dynamics-paper

Chaos as an interpretable benchmark for forecasting and data-driven modelling

https://github.com/sotif-avlab/pesotif

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/sriloksagar/fashion-mnist

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

https://github.com/sswetank-CS/MIL

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

https://github.com/stanfordvl/atp-video-language

Revisiting the "Video" in Video-Language Understanding

https://github.com/starry-sky6688/marl-algorithms

The StarCraft Multi-Agent Challenge

https://github.com/stevenboys/moon

OpenOOD: Benchmarking Generalized Out-of-Distribution Detection

https://github.com/stovecat/convcodeworld

BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

https://github.com/suilin0432/mmdetectionWithCodeAnnotations

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/super5-22/habitat-vis

Habitat: A Platform for Embodied AI Research

https://github.com/swarooprm/DQI

DQI: Measuring Data Quality in NLP

https://github.com/symbioticlab/zeus

The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization

https://github.com/taited/clip-score

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/tajwarfahim/proactive_interventions

Autonomous Reinforcement Learning: Formalism and Benchmarking

https://github.com/takashiishida/irreducible

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

https://github.com/tamerthamoqa/3D-mri-brain-tumour-image-segmentation-medical-decathlon-tensorflow

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

https://github.com/tanny411/llm-reliability-and-consistency-evaluation

TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability

https://github.com/tarun-munjal/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/tawald/nnunet

nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation

https://github.com/taylorhawks/RNN-music-recommender

Sequence-Aware Recommender Systems

https://github.com/taylorhawks/Recommender

Sequence-Aware Recommender Systems

https://github.com/tedmoskovitz/DOPE

Benchmarks for Deep Off-Policy Evaluation

https://github.com/tedmoskovitz/TOP

Benchmarks for Deep Off-Policy Evaluation

https://github.com/tensorflow/models

Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

https://github.com/tensorflow/models/tree/master/research/delf

Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

https://github.com/testingautomated-usi/fashion-mnist-c

MNIST-C: A Robustness Benchmark for Computer Vision

https://github.com/thangvubk/SCNet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/thu-ml/realsafe

RobFR: Benchmarking Adversarial Robustness on Face Recognition

https://github.com/thudm/codegeex2

CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X

https://github.com/thuiar/textoir

An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction

https://github.com/thuiar/textoir-demo

An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction

https://github.com/thxsxth/POMDP_RLSepsis

Benchmarking Batch Deep Reinforcement Learning Algorithms

https://github.com/tianchiVideoSeg/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/tiangexiang/CurveNet

Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions

https://github.com/tianhai123/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/tianyaoZhang/DeeCamp

MMDetection: Open MMLab Detection Toolbox and Benchmark

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