Papers with Code 아카이브
Computational Efficiency
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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/jinx-USTC/GI-ReID
공식 코드Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization
https://github.com/jinyeran/source-free_active_domain_adaptation
공식 코드https://github.com/jjaeyeon/gebo
공식 코드Mold into a Graph: Efficient Bayesian Optimization over Mixed-Spaces
https://github.com/jjunak-yun/FLowHigh_code
공식 코드FLowHigh: Towards Efficient and High-Quality Audio Super-Resolution with Single-Step Flow Matching
https://github.com/jl-er/bone
공식 코드Balancing LoRA Performance and Efficiency with Simple Shard Sharing
https://github.com/jl-er/disha
공식 코드Balancing LoRA Performance and Efficiency with Simple Shard Sharing
https://github.com/jl-er/modrwkv
공식 코드https://github.com/jmjmalik22/hate-speech-detection
공식 코드Deep Learning for Hate Speech Detection: A Comparative Study
https://github.com/johanye/vf-net
공식 코드https://github.com/john-ai-lab/rapid
공식 코드Long-Context Inference with Retrieval-Augmented Speculative Decoding
https://github.com/john1226966735/adaptive-solver
공식 코드Adaptive-Solver Framework for Dynamic Strategy Selection in Large Language Model Reasoning
https://github.com/johnolafenwa/FastNet
공식 코드https://github.com/johnromanelis/spvd
공식 코드Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models
https://github.com/jonas-koehler/s2cnn
공식 코드https://github.com/jonas-sm/td-mpc-srl
공식 코드Model Predictive Control with Self-supervised Representation Learning
https://github.com/jonghyunharrylee/pyPCGA
공식 코드https://github.com/jonhehir/acsbm
공식 코드https://github.com/jordan7186/page
공식 코드PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks
https://github.com/jorgemarcoes/r-clustering
공식 코드https://github.com/joseangelmartinb/prediction-behavioural-analysis-ml-travel-mode-choice
공식 코드A prediction and behavioural analysis of machine learning methods for modelling travel mode choice
https://github.com/joshua840/robustaga
공식 코드Towards More Robust Interpretation via Local Gradient Alignment
https://github.com/josiahwsmith10/thz-and-sub-thz-imaging-toolbox
공식 코드https://github.com/jpp46/alife2018
공식 코드Exploring the effects of robotic design on learning and neural control
https://github.com/juliabinarywrappers/coverm_jll.jl
공식 코드https://github.com/junaidmalik09/fastonn
공식 코드Self-Organized Operational Neural Networks with Generative Neurons
https://github.com/junkangwu/alpha-dpo
공식 코드$α$-DPO: Adaptive Reward Margin is What Direct Preference Optimization Needs
https://github.com/junlianglv/task_diversity_bfl
공식 코드https://github.com/junpan19/RRN
공식 코드https://github.com/junqizhao888/buzz-llm
공식 코드BUZZ: Beehive-structured Sparse KV Cache with Segmented Heavy Hitters for Efficient LLM Inference
https://github.com/juntaojianggavin/rwkv-unet
공식 코드RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation
https://github.com/junyuchen245/pretraining_image_registration_dnns
공식 코드Pretraining Deformable Image Registration Networks with Random Images
https://github.com/junzengz/rmamamba
공식 코드A Reverse Mamba Attention Network for Pathological Liver Segmentation
https://github.com/jusperlee/apollo
공식 코드Apollo: Band-sequence Modeling for High-Quality Audio Restoration
https://github.com/jw-chae/plddt_predictor
공식 코드pLDDT-Predictor: High-speed Protein Screening Using Transformer and ESM2
https://github.com/jwr1995/pubsep
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.