https://github.com/anonymous7852/unibrain
공식 코드End-to-End Deep Learning for Structural Brain Imaging: A Unified Framework
Papers with Code 아카이브
Computational Efficiency
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 1
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/another-0/dmm
공식 코드DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing
https://github.com/anr-mimico/jfsma_schelling
공식 코드Modèles de Substitution pour les Modèles à base d'Agents : Enjeux, Méthodes et Applications
https://github.com/anselmeamekoe/delayedlabelstream
공식 코드https://github.com/antonbaumann/mimo-unet
공식 코드Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression
https://github.com/antoninschrab/ksdagg
공식 코드Efficient Aggregated Kernel Tests using Incomplete $U$-statistics
https://github.com/antonior92/NarmaxLasso.jl
공식 코드Lasso Regularization Paths for NARMAX Models via Coordinate Descent
https://github.com/aoi3142/waterfall
공식 코드Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs
https://github.com/aorliche/latentsimilarity
공식 코드Latent Similarity Identifies Important Functional Connections for Phenotype Prediction
https://github.com/apcamargo/pycoverm
공식 코드https://github.com/apple/ml-tune-mode-convbn
공식 코드https://github.com/aravind0706/flowpp
공식 코드https://github.com/aravindsrinivas/flowpp
공식 코드https://github.com/arclab-mit/t-pdg
공식 코드https://github.com/areuther/ai-accelerators
공식 코드https://github.com/ari-dasci/S-EvoPruneDeepTL
공식 코드EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural Networks
https://github.com/ari-dasci/S-layerwise_cosine_aggregation
공식 코드https://github.com/articulatory/articulatory
공식 코드Deep Speech Synthesis from MRI-Based Articulatory Representations
https://github.com/artiprocher/sd-webui-fastblend
공식 코드FastBlend: a Powerful Model-Free Toolkit Making Video Stylization Easier
https://github.com/arturjordao/IncrementalDimensionalityReduction
공식 코드Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method
https://github.com/asarigun/GraphMixerNetworks
공식 코드https://github.com/ashu1069/perceptual-piercing
공식 코드Perceptual Piercing: Human Visual Cue-based Object Detection in Low Visibility Conditions
https://github.com/asperti/alpha_flops_dataset
공식 코드Dissecting FLOPs along input dimensions for GreenAI cost estimations
https://github.com/aswinpaul/dpefe_2023
공식 코드https://github.com/athulshibu/Rewarded-meta-pruning
공식 코드Rewarded meta-pruning: Meta Learning with Rewards for Channel Pruning
https://github.com/atik-ahamed/timemachine
공식 코드TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
https://github.com/atishdixit16/ada_multigrid_ppo
공식 코드Robust optimal well control using an adaptive multi-grid reinforcement learning framework
https://github.com/atlasanalyticslab/2dmamba
공식 코드https://github.com/attentionseekers/cnntention
공식 코드https://github.com/au-revoir/model-editing-ft
공식 코드https://github.com/audio-westlakeu/fullsubnet
공식 코드https://github.com/audiolabs/DEISM
공식 코드https://github.com/aurora2050/cobay-cpd
공식 코드Conjugate Bayesian Two-step Change Point Detection for Hawkes Process
https://github.com/aurtg/learning-on-grammar-compression
공식 코드https://github.com/autodeskailab/wala
공식 코드https://github.com/avhixd/human_pose_estimation
공식 코드Efficient Human Pose Estimation: Leveraging Advanced Techniques with MediaPipe
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.