https://github.com/rivas-lab/Smiles2Dock
공식 코드Smiles2Dock: an open large-scale multi-task dataset for ML-based molecular docking
Papers with Code 아카이브
Benchmarking
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rkarmaka/SoftPQ
공식 코드SoftPQ: Robust Instance Segmentation Evaluation via Soft Matching and Tunable Thresholds
https://github.com/rll-research/b-pref
공식 코드B-Pref: Benchmarking Preference-Based Reinforcement Learning
https://github.com/rllab/rllab
공식 코드Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/robfiras/loco-mujoco
공식 코드LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion
https://github.com/robin-janssen/codes-benchmark
공식 코드https://github.com/robshahla/VisQUIC
공식 코드Exploring QUIC Dynamics: A Large-Scale Dataset for Encrypted Traffic Analysis
https://github.com/rockylzy/textdefender
공식 코드Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution
https://github.com/roeltim/ml-leaderboard-position-paper
공식 코드A Position Paper on the Automatic Generation of Machine Learning Leaderboards
https://github.com/rohban-lab/swinchex
공식 코드SwinCheX: Multi-label classification on chest X-ray images with transformers
https://github.com/rohit901/VANE-Bench
공식 코드VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs
https://github.com/rolnicklab/imagenetbiodiversity
공식 코드https://github.com/romanenkova95/principledcpd
공식 코드Deep learning model solves change point detection for multiple change types
https://github.com/roundfacej/omni
공식 코드https://github.com/rovislab/loopdb
공식 코드LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping
https://github.com/rr-learning/trifinger-rl-example
공식 코드Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
https://github.com/rr-learning/trifinger_rl_datasets
공식 코드Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
https://github.com/rramosp/llpeo
공식 코드On-orbit model training for satellite imagery with label proportions
https://github.com/rsantana-isg/morl_benchmark
공식 코드Benchmarking MOEAs for solving continuous multi-objective RL problems
https://github.com/rtu715/nas-bench-360
공식 코드NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
https://github.com/ruathudo/tfw2v
공식 코드TFW2V: An Enhanced Document Similarity Method for the Morphologically Rich Finnish Language
https://github.com/rubenrtorrado/GVGAI_GYM
공식 코드https://github.com/rucaibox/recbole2.0
공식 코드RecBole 2.0: Towards a More Up-to-Date Recommendation Library
https://github.com/rvandewater/recipys
공식 코드Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML
https://github.com/rvandewater/yaib
공식 코드Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML
https://github.com/rvandewater/yaib-cohorts
공식 코드Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML
https://github.com/rvandewater/yaib-models
공식 코드Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML
https://github.com/rvivek3/anchorpoints
공식 코드https://github.com/rvp-group/vbr-devkit
공식 코드https://github.com/rwgai/rwds
공식 코드Benchmarking Object Detectors under Real-World Distribution Shifts in Satellite Imagery
https://github.com/ryienh/gnn-ops-benchmark
공식 코드Operation-Level Performance Benchmarking of Graph Neural Networks for Scientific Applications
https://github.com/ryliu68/igmu
공식 코드https://github.com/ryojitanabe/as_bbo
공식 코드Benchmarking Feature-based Algorithm Selection Systems for Black-box Numerical Optimization
https://github.com/ryojitanabe/de_bbobmixint
공식 코드https://github.com/ryojitanabe/largebbob2022
공식 코드Benchmarking the Hooke-Jeeves Method, MTS-LS1, and BSrr on the Large-scale BBOB Function Set
https://github.com/ryojitanabe/prefqi
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.