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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/QData/TextAttack
공식 코드https://github.com/QData/TextAttack-Search-Benchmark
공식 코드https://github.com/QDucasse/nn_benchmark
공식 코드https://github.com/RLGC-Project/RLGC
공식 코드Adaptive Power System Emergency Control using Deep Reinforcement Learning
https://github.com/Raman1121/diffusion_memorization
공식 코드https://github.com/RelationRx/pyrelational
공식 코드PyRelationAL: a python library for active learning research and development
https://github.com/Reliable-Information-Lab-HEVS/llm_detectors_fall_short
공식 코드LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts
https://github.com/RetailMarketingAI/retailsynth-agentsim
공식 코드Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail Promotions
https://github.com/Robert-Morabito/STOP
공식 코드STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions
https://github.com/RobustBench/robustbench
공식 코드RobustBench: a standardized adversarial robustness benchmark
https://github.com/Rohit102497/Aux-Drop
공식 코드Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts
https://github.com/RolnickLab/climart
공식 코드https://github.com/Ruifeng-Tan/BatteryLife
공식 코드BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction
https://github.com/SCAlabUnical/CLEAR-Bias_LLM_benchmark
공식 코드https://github.com/SIP-Lab/Deep-Learning-Mobile
공식 코드Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps
https://github.com/SM4DA/CheMFi
공식 코드QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules
https://github.com/SM4DA/MFDeltaML
공식 코드Benchmarking Data Efficiency in $Δ$-ML and Multifidelity Models for Quantum Chemistry
https://github.com/SUTDCV/SUTD-TrafficQA
공식 코드https://github.com/Saumajit/BanglaNLP/tree/main/Task_2
공식 코드https://github.com/Seed3D/Dora
공식 코드Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders
https://github.com/ServiceNow/repliqa
공식 코드RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content
https://github.com/ShawnXYang/Face-Robustness-Benchmark
공식 코드RobFR: Benchmarking Adversarial Robustness on Face Recognition
https://github.com/Simonlee711/FEET
공식 코드https://github.com/SirRob1997/Descending-through-a-Crowded-Valley---Results
공식 코드Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers
https://github.com/SongBaiHust/Adversarial_Metric_Attack
공식 코드Adversarial Metric Attack and Defense for Person Re-identification
https://github.com/Soumyabrata/HDR-cloud-segmentation
공식 코드https://github.com/Spiegeldondi/A-Hybrid-Quantum-Classical-Framework-for-Reinforcement-Learning-of-Atari-Games
공식 코드A quantum-classical reinforcement learning model to play Atari games
https://github.com/StatPhysBio/protein_holography-web
공식 코드HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction
https://github.com/Steven51516/FlexMol
공식 코드FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning
https://github.com/SymbioticLab/FedScale
공식 코드FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
https://github.com/SysCV/tet
공식 코드https://github.com/TGB-Seq/TGB-Seq
공식 코드TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics
https://github.com/THU-KEG/BIMR
공식 코드Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability
https://github.com/THU-KEG/MAVEN-dataset
공식 코드https://github.com/THU-KEG/WaterBench
공식 코드WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.