https://github.com/dmlc/dgl/tree/master/examples/pytorch/cluster_gcn
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
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/doctelligence/federated-learning
BAFFLE : Blockchain Based Aggregator Free Federated Learning
https://github.com/dsikar/efficient-compact-fire-detection-cnn
https://github.com/dtuia/KEMA
https://github.com/e-solutions-GmbH/TensorFlow-iEVM
Exploring the Open World Using Incremental Extreme Value Machines
https://github.com/eBay/bayesian-belief-networks
https://github.com/eddelbuettel/rcppcorels
https://github.com/ennengyang/surgeryv2
https://github.com/eowjd0512/VITAMIN-E
VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense Feature Points
https://github.com/eric-erki/robotics-rl-srl
Simple random search provides a competitive approach to reinforcement learning
https://github.com/esraaelelimy/LRU
https://github.com/eth-easl/mixtera
Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws
https://github.com/fabianwinter93/JAX
https://github.com/facebookresearch/fairscale
https://github.com/facebookresearch/rl
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
https://github.com/fchollet/deep-learning-models
https://github.com/fingoldin/pycorels
https://github.com/fliegenpilz357/amr-metric-suite
https://github.com/flipz357/amr-metric-suite
https://github.com/forgi86/lru-reduction
https://github.com/frederikkemarin/bend
DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome
https://github.com/fredfung007/cityflow-nl
https://github.com/frotms/PaddleOCR2Pytorch
https://github.com/fschmid56/efficientat_hear
Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation
https://github.com/fskong/FT-Classifier-for-Model-Merging
https://github.com/fudan-zvg/SOFT_MindSpore_Ascend
https://github.com/furyhawk/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/fuweijie/Paper-on-Large-scale-Machine-Learning
https://github.com/gair-nlp/maye
REINFORCE++: A Simple and Efficient Approach for Aligning Large Language Models
https://github.com/gellston/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/gleefe1995/GCNv2
GCNv2: Efficient Correspondence Prediction for Real-Time SLAM
https://github.com/gmberton/VPR-datasets-downloader
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition
https://github.com/google/evojax
Simple random search provides a competitive approach to reinforcement learning
https://github.com/gpspelle/Convert_channel
https://github.com/greenbitai/low_bit_llama
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
https://github.com/guome/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.