https://github.com/Xingyu-Lin/softgym
공식 코드SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation
Papers with Code 아카이브
Data-free Knowledge Distillation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Y-L-LIU/MGTBench-2.0
공식 코드On the Generalization Ability of Machine-Generated Text Detectors
https://github.com/YLab-Open/BRIDGE
공식 코드BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
https://github.com/YecanLee/2BeOETG
공식 코드Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework
https://github.com/YihanWang617/context_awareness
공식 코드On the Loss of Context-awareness in General Instruction Fine-tuning
https://github.com/Yinghao-Li/UncertaintyBenchmark
공식 코드MUBen: Benchmarking the Uncertainty of Molecular Representation Models
https://github.com/ZamDimon/liveness-detection
공식 코드https://github.com/ZhangYuanhan-AI/OmniBenchmark
공식 코드Benchmarking Omni-Vision Representation through the Lens of Visual Realms
https://github.com/ZhaoJ9014/High_Performance_Face_Recognition
공식 코드https://github.com/Zhaolu-K/HSSBench
공식 코드HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
https://github.com/ZhaomingKong/Denoising-Comparison
공식 코드https://github.com/Zheyu-Rain/APT-HLS-AI
공식 코드https://github.com/a-rebmann/llms4pm
공식 코드Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks
https://github.com/a11to1n3/LimeSoDa
공식 코드https://github.com/aaandy-zhu/vgcure
공식 코드https://github.com/aabarr/Synthetic-Neurosurgical-Data
공식 코드Zero-shot generation of synthetic neurosurgical data with large language models
https://github.com/aadharna/watts
공식 코드https://github.com/aalto-speech/dbca
공식 코드https://github.com/aashish-khub/NearestNeighbors
공식 코드N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
https://github.com/aashish2000/anna
공식 코드ANNA: Abstractive Text-to-Image Synthesis with Filtered News Captions
https://github.com/abacusai/xai-bench
공식 코드Synthetic Benchmarks for Scientific Research in Explainable Machine Learning
https://github.com/abdullahsohaill/cs6303-researchproject
공식 코드Deciphering the Underserved: Benchmarking LLM OCR for Low-Resource Scripts
https://github.com/abhi1nandy2/yesbut_dataset
공식 코드https://github.com/abhipec/HAnDS
공식 코드Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage
https://github.com/abin333/logonet
공식 코드LogoNet: a fine-grained network for instance-level logo sketch retrieval
https://github.com/aboveparadise/llmcbench
공식 코드LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment
https://github.com/abr/power_benchmarks
공식 코드Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware
https://github.com/abrain-one/nn-dataset
공식 코드https://github.com/abrain-one/nn-plots
공식 코드https://github.com/abrain-one/nn-vr
공식 코드https://github.com/acadlab/spicepilot
공식 코드SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance
https://github.com/acellera/acegen-open
공식 코드ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
https://github.com/acmislab/pediabench
공식 코드PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language Models
https://github.com/adaptive-agents-lab/mdot-pncg
공식 코드Efficient and Accurate Optimal Transport with Mirror Descent and Conjugate Gradients
https://github.com/adaptive-intelligent-robotics/qdax
공식 코드Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning
https://github.com/aditya-grover/climate-learn
공식 코드ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.