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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/muleina/anomalycd
공식 코드https://github.com/multimodallearning/denoised_mt_pcd_reg
공식 코드A denoised Mean Teacher for domain adaptive point cloud registration
https://github.com/mungomeng/registration-aan
공식 코드Enhancing Medical Image Registration via Appearance Adjustment Networks
https://github.com/musicalOffering/sola
공식 코드https://github.com/muzairzahid/r-peak-detection-1d-selfonn
공식 코드Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks
https://github.com/mxxxr/mgfwa
공식 코드https://github.com/myuli/sst
공식 코드Spatial-Spectral Transformer for Hyperspectral Image Denoising
https://github.com/mzeromiko/vmamba
공식 코드https://github.com/nagejacob/tbsn
공식 코드Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising
https://github.com/nannanbian/cpnet
공식 코드Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting
https://github.com/nanyomy/cmmas
공식 코드Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion
https://github.com/naoufalEB/FlowKac
공식 코드https://github.com/nascimentog/pruningeverything
공식 코드https://github.com/nash169/demo-learn-embedding
공식 코드Learning Dynamical Systems Encoding Non-Linearity within Space Curvature
https://github.com/nash169/learn-embedding
공식 코드Learning Dynamical Systems Encoding Non-Linearity within Space Curvature
https://github.com/nathankutz/plasmashred
공식 코드Shallow Recurrent Decoder for Reduced Order Modeling of Plasma Dynamics
https://github.com/navid-naderi/WEGL
공식 코드https://github.com/ndexter/MLFA
공식 코드The gap between theory and practice in function approximation with deep neural networks
https://github.com/neitzal/adaptive-skip-intervals
공식 코드Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
https://github.com/neu-real/oba
공식 코드https://github.com/neurai-lab/progressivedecoderfusion
공식 코드Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature Sharing
https://github.com/neuraloperator/neuraloperator
공식 코드Enhancing Fourier Neural Operators with Local Spatial Features
https://github.com/neurodata/R-RerF
공식 코드https://github.com/newbeeyoung/video-corruption-robustness
공식 코드Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions
https://github.com/newbeezzc/MonarchAttn
공식 코드Enhancing Transformer-based models for Long Sequence Time Series Forecasting via Structured Matrix
https://github.com/news-vt/makassar-ml
공식 코드A Transformer Framework for Data Fusion and Multi-Task Learning in Smart Cities
https://github.com/nhattlm95/LLM_for_Classroom_Discussion
공식 코드Analyzing Large Language Models for Classroom Discussion Assessment
https://github.com/nickwzk/streamsplat
공식 코드StreamSplat: Towards Online Dynamic 3D Reconstruction from Uncalibrated Video Streams
https://github.com/nightsnack/YOLObile
공식 코드YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
https://github.com/nikhilvyas/soap
공식 코드https://github.com/niklasschmitz/ad-kernels
공식 코드Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
https://github.com/njunlp/pats
공식 코드https://github.com/nklb/stochastic-smad
공식 코드Data-based stochastic modeling reveals sources of activity bursts in single-cell TGF-$β$ signaling
https://github.com/nkmjm/qiML
공식 코드Quantum-inspired canonical correlation analysis for exponentially large dimensional data
https://github.com/nkpanda97/ul-flexibility
공식 코드Efficient Quantification and Representation of Aggregate Flexibility in Electric Vehicles
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.