Papers with Code 아카이브
Computational Efficiency
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/modelscope/modelscope
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
https://github.com/mpuu00001/Siformer
Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition
https://github.com/mseg-dataset/mseg-api
MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
https://github.com/msgwak/last
https://github.com/multi-swe-bench/magentless
Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts
https://github.com/multi-swe-bench/mopenhands
Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts
https://github.com/multi-swe-bench/mswe-agent
Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts
https://github.com/mx-mark/dmjd
https://github.com/myarist/Rock-Paper-Scissors
https://github.com/myuli/sert
Spatial-Spectral Transformer for Hyperspectral Image Denoising
https://github.com/mziad97/Frog-v-Giraffe
https://github.com/nabsabraham/focal-tversky-unet
https://github.com/neitzal/asi-tasks
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
https://github.com/nelson1425/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/neurodata/LOL
https://github.com/neurodata/SPORF
https://github.com/neurodata/lollipop
https://github.com/neurodata/mgc-r
Discovering and Deciphering Relationships Across Disparate Data Modalities
https://github.com/neurodata/mgcpy
Discovering and Deciphering Relationships Across Disparate Data Modalities
https://github.com/neurodata/r-mgc
Discovering and Deciphering Relationships Across Disparate Data Modalities
https://github.com/nicolaszucchet/minimal-lru
https://github.com/niranjana98/Image-Classification
https://github.com/nlarusstone/corels
https://github.com/nnaisense/pgpelib
Simple random search provides a competitive approach to reinforcement learning
https://github.com/nobodyplayer1/vm-unetv2
U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation
https://github.com/nourmorsy/PremioLLM
Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language Models
https://github.com/nsinghal7/Kimera-VIO-NetVLAD
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/nsinghal7/Kimera-VIO-ROS-NetVLAD
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/ntkhoa95/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/nus-hpc-ai-lab/dyvm
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/nvlabs/afno-transformer
Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
https://github.com/octothrope/ARS
Simple random search provides a competitive approach to reinforcement learning
https://github.com/omerjerk/cuSABNAtk
https://github.com/open-mmlab/mmclassification
https://github.com/open-mmlab/mmsegmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.