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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Avmb/semantic_neq_game
공식 코드Program Semantic Inequivalence Game with Large Language Models
https://github.com/Leolty/repobench
공식 코드RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems
https://github.com/MurpheyLab/CPL-SLAM
공식 코드https://github.com/NVlabs/SegFormer
공식 코드SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/SC-SGS/GPRat
공식 코드https://github.com/SteveKommrusch/VRepair
공식 코드Neural Transfer Learning for Repairing Security Vulnerabilities in C Code
https://github.com/bin123apple/fortran-cpp-hpc-code-translation-dataset
공식 코드https://github.com/eml-eda/eden
공식 코드Dynamic Decision Tree Ensembles for Energy-Efficient Inference on IoT Edge Nodes
https://github.com/esbmc/esbmc
공식 코드https://github.com/google-research/self-organising-systems/tree/master/notebooks/
공식 코드$μ$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata
https://github.com/hpc-fortran2cpp/fortran2cpp
공식 코드https://github.com/jordiae/neural-compilers
공식 코드Learning C to x86 Translation: An Experiment in Neural Compilation
https://github.com/juliacontrol/discretepids.jl
공식 코드https://github.com/jungyhuk/latent-execution
공식 코드https://github.com/liangwu2019/eiqp
공식 코드EIQP: Execution-time-certified and Infeasibility-detecting QP Solver
https://github.com/michiyasunaga/bifi
공식 코드https://github.com/petitioner/he.cnntraining
공식 코드Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regression
https://github.com/plasma-umass/chatdbg
공식 코드https://github.com/project-monai/research-contributions
공식 코드https://github.com/pulp-platform/dory
공식 코드DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs
https://github.com/rubengooj/pl-slam
공식 코드PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments
https://github.com/sea-lab/hybrid-net
공식 코드Hybrid Deep Neural Networks to Infer State Models of Black-Box Systems
https://github.com/shah314/gamultiknapsack
공식 코드Genetic Algorithm for the 0/1 Multidimensional Knapsack Problem
https://github.com/tech-srl/slm-code-generation
공식 코드https://github.com/thudm/scenegenagent
공식 코드SceneGenAgent: Precise Industrial Scene Generation with Coding Agent
https://github.com/2023-MindSpore-4/Code11/tree/main/Segformer
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/2023-MindSpore-4/Code11/tree/main/SemanticHumanMatting
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/2023-MindSpore-4/Code14/tree/main/Segformer
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/BR-IDL/PaddleViT/tree/main/semantic_segmentation
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/DarshanDeshpande/jax-models
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/DavidLandup0/deepvision
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/GhadeerElmkaiel/SegFormer
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/IMvision12/SegFormer-tf
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/IMvision12/keras-vision-models
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/JoegameZhou/Segmenter_workers
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.