https://github.com/wenlongjiao/frenet
공식 코드Efficient RAW Image Deblurring with Adaptive Frequency Modulation
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/wentaotan/interleaved-learning
공식 코드Style Interleaved Learning for Generalizable Person Re-identification
https://github.com/wertzsamuel/hadamarddecompositions
공식 코드https://github.com/western-oc2-lab/evci-pruning
공식 코드https://github.com/westlakeintelligentrobotics/glad
공식 코드Global-Local MAV Detection under Challenging Conditions based on Appearance and Motion
https://github.com/whlzy/fit
공식 코드https://github.com/whu-sigma/hypersigma
공식 코드HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model
https://github.com/windborne/weathermesh-3
공식 코드WeatherMesh-3: Fast and accurate operational global weather forecasting
https://github.com/wintersun661/MMNet
공식 코드https://github.com/withray/residualkan
공식 코드Residual Kolmogorov-Arnold Network for Enhanced Deep Learning
https://github.com/wmdataphys/bayesian-hr-kan
공식 코드Uncertainty Quantification with Bayesian Higher Order ReLU KANs
https://github.com/wokas36/structposgssl
공식 코드Graph Self-Supervised Learning with Learnable Structural and Positional Encodings
https://github.com/workday/cpc
공식 코드Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inference
https://github.com/wrik/cnn-optimization
공식 코드Deep convolutional neural network for shape optimization using level-set approach
https://github.com/wujiong-hub/ldm-morph
공식 코드LDM-Morph: Latent diffusion model guided deformable image registration
https://github.com/wwood/coverm
공식 코드https://github.com/wwydmanski/GFSNetwork
공식 코드GFSNetwork: Differentiable Feature Selection via Gumbel-Sigmoid Relaxation
https://github.com/wxy11-27/gmsr
공식 코드GMSR:Gradient-Guided Mamba for Spectral Reconstruction from RGB Images
https://github.com/wyd0042/EchoMamba4Rec
공식 코드https://github.com/wyl010607/FilterTS
공식 코드FilterTS: Comprehensive Frequency Filtering for Multivariate Time Series Forecasting
https://github.com/wzhwzhwzh0921/s-d-mamba
공식 코드https://github.com/wzjialang/sedmamba
공식 코드https://github.com/xarakas/spheroid-tnf-v2-emews
공식 코드https://github.com/xbjin/cgvr
공식 코드Stochastic Conjugate Gradient Algorithm with Variance Reduction
https://github.com/xiangxdai/combinatorial-logistic-bandit
공식 코드https://github.com/xiangxu-0103/superflow
공식 코드SuperFlow++: Enhanced Spatiotemporal Consistency for Cross-Modal Data Pretraining
https://github.com/xiaoyao3302/pointramba
공식 코드PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis
https://github.com/xiekkki/motionbev
공식 코드https://github.com/xiexi1990/iccad-accel-gnn
공식 코드Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
https://github.com/xinjinghao/real-time-path-planning-with-sepso
공식 코드https://github.com/xinyi-yu/mpc4stl-tid
공식 코드Model Predictive Control for Signal Temporal Logic Specifications with Time Interval Decomposition
https://github.com/xiongjiechen/ASWD
공식 코드https://github.com/xiuqhou/Salience-DETR
공식 코드Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement
https://github.com/xiyan1234567/smetimes
공식 코드Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMs
https://github.com/xmed-lab/gptrack
공식 코드Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding
https://github.com/xmindflow/lhunet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.