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/ray-project/ray/tree/master/rllib
Simple random search provides a competitive approach to reinforcement learning
https://github.com/raychiu0202/paddleOCR_flask
https://github.com/raytrun/mamba-clip
https://github.com/redrock303/NeRFLiX_CPVR2023
From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm
https://github.com/reeered/LRU-MindSpore
https://github.com/rezazad68/BCDU-Net
https://github.com/rickyegl/BeyondTheRainbowICLR
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC
https://github.com/rickyegl/BeyondTheRainbowM
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC
https://github.com/rizalmaulanaa/robustness_of_prob_u_net
https://github.com/rohdelab/PyTransKit
Radon cumulative distribution transform subspace modeling for image classification
https://github.com/rohdelab/rcdt_ns_classifier_matlab
Radon cumulative distribution transform subspace modeling for image classification
https://github.com/rolnicklab/ocp
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design
https://github.com/rwightman/pytorch-image-models
https://github.com/rwkv/rwkv-lm
https://github.com/rwkv/rwkv.cpp
https://github.com/rximg/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/ryojitanabe/ela_drframework
High Dimensional Bayesian Optimization Assisted by Principal Component Analysis
https://github.com/sala-group/autows-bench-101
https://github.com/saligrama/rcorels
https://github.com/sarmueller/gibo
Simple random search provides a competitive approach to reinforcement learning
https://github.com/sbodenstein/Tutorials
Simple random search provides a competitive approach to reinforcement learning
https://github.com/scikit-learn-contrib/polylearn
https://github.com/seanmcgovern21/Machine-Learning-CS539
Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks
https://github.com/securade/hub
https://github.com/seongho608/ringformer
https://github.com/seung7361/RWKV-Pytorch
https://github.com/sfczekalski/attention_unet
https://github.com/shaonianruntu/MWCNN
https://github.com/shendb2022/htd-mamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/shinpoi/pixiv_collector_old
https://github.com/shriramsb/batchRL-SI
https://github.com/shuo-git/GraphGAN
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
https://github.com/simplify23/Ultra_light_OCR_No.11
https://github.com/sirmarcel/glp
https://github.com/skiteskopes/FID_CULLING
Training Domain Specific Models for Energy-Efficient Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.