Papers with Code 아카이브
Computational Efficiency
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/atspatel/pydata_inception_v2
https://github.com/aulrichsen/sronn
Self-Organized Operational Neural Networks with Generative Neurons
https://github.com/autumn9999/go4align
https://github.com/avrahamraviv/formal-verification-of-object-detection
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/ayoub-sudo/Attention-Unet-web-app
https://github.com/ayushdabra/stanford-dogs-dataset-classification
https://github.com/ayushgupta9198/gan
https://github.com/ayushgupta9198/stylegan
https://github.com/baekjin-kim/UAI2020_NonstationaryLinearBandit
Randomized Exploration for Non-Stationary Stochastic Linear Bandits
https://github.com/bakerv/fer-webcam
Facial Emotion Recognition: State of the Art Performance on FER2013
https://github.com/baowenbo/MEMC-Net
https://github.com/bayesian-object-tracking/dbot
https://github.com/benedekrozemberczki/ClusterGCN
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
https://github.com/benedekrozemberczki/karateclub
A simple yet effective baseline for non-attributed graph classification
https://github.com/benedekrozemberczki/littleballoffur
https://github.com/bhuyanamit986/FlowerClassification
https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets
https://github.com/billchan226/poar-srl-4-robot
Simple random search provides a competitive approach to reinforcement learning
https://github.com/birdwatcherYT/Learning-Interpretable-Metric-between-Graphs
https://github.com/bl166/wireless_digital_twin_j
Learnable Digital Twin for Efficient Wireless Network Evaluation
https://github.com/bo-10000/VoxResNet
https://github.com/bobye/WBC_Matlab
Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support
https://github.com/bojone/rnn
https://github.com/bowang-lab/graph-mamba
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
https://github.com/boyu-zhang-25/DropConnect_DPP
https://github.com/brainless-afk/Binary_Stereo_Matching
https://github.com/brandontrabucco/im2txt_express
https://github.com/brandontrabucco/im2txt_match
https://github.com/britacatalin/bsa
Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems
https://github.com/bruinxiong/EG3D-pytorch
https://github.com/bryanyzhu/two-stream-pytorch
https://github.com/buyeah1109/KEN
https://github.com/buyeah1109/finc
https://github.com/calmisential/TensorFlow2.0_InceptionV3
https://github.com/cambridge-mlg/miracle
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.