https://github.com/AegisIK/DistMLIP
공식 코드DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Papers with Code 아카이브
graph partitioning
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Graph Partitioning is generally the first step of distributed graph computing tasks. The targets are load-balance and minimizing the communication volume.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CompNet/ExtMeasEval
공식 코드https://github.com/HongtengXu/s-gwl
공식 코드Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
https://github.com/Joinn99/FPSR
공식 코드Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation
https://github.com/KaHIP/KaHIP
공식 코드https://github.com/S1mple-yyds/Fed_Epi_Classifier
공식 코드Towards the efficacy of federated prediction for epidemics on networks
https://github.com/TheLeprechaun25/neural-improvement-heuristics
공식 코드Neural Improvement Heuristics for Graph Combinatorial Optimization Problems
https://github.com/alga-hopf/drl-graph-partitioning
공식 코드Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks
https://github.com/alibaba-nlp/key-point-analysis
공식 코드Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning
https://github.com/anshuman23/cfc
공식 코드Robust Fair Clustering: A Novel Fairness Attack and Defense Framework
https://github.com/antoniochaves19/rk-grasp
공식 코드https://github.com/bath-reinforcement-learning-lab/louvain-skills-neurips-2023
공식 코드Creating Multi-Level Skill Hierarchies in Reinforcement Learning
https://github.com/benedekrozemberczki/pytorch_geometric_temporal
공식 코드PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training
https://github.com/bit-ml/sfseg
공식 코드A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time
https://github.com/constantinpape/cluster_tools
공식 코드Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts
https://github.com/desh2608/dover-lap
공식 코드Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem
https://github.com/dmlc/dgl
공식 코드DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
https://github.com/farnooshar/specuniis
공식 코드Deep Spectral Improvement for Unsupervised Image Instance Segmentation
https://github.com/farnooshar/specunvis
공식 코드Deep Spectral Improvement for Unsupervised Image Instance Segmentation
https://github.com/happy2git/guide
공식 코드https://github.com/hping666/HSDAG
공식 코드A Structure-Aware Framework for Learning Device Placements on Computation Graphs
https://github.com/huangdonghere/fastmice
공식 코드Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity
https://github.com/idea-iitd/neurocut
공식 코드https://github.com/jiahaoLjh/PoseGrouping
공식 코드Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping
https://github.com/jiali2000/fnm
공식 코드Spectral Normalized-Cut Graph Partitioning with Fairness Constraints
https://github.com/jiaweihe1996/GMTracker
공식 코드https://github.com/jsshin98/ssgnet
공식 코드https://github.com/kuroginqin/prgpt
공식 코드Towards Faster Graph Partitioning via Pre-training and Inductive Inference
https://github.com/larsgottesbueren/gp-ann
공식 코드Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search
https://github.com/liyaguang/DCRNN
공식 코드https://github.com/lukemelas/deep-spectral-segmentation
공식 코드https://github.com/luorenqiang/fairgp
공식 코드FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning
https://github.com/mashaan14/ASC-noisy
공식 코드Approximate spectral clustering density-based similarity for noisy datasets
https://github.com/mashaan14/ASC-self-tuned-k
공식 코드Approximate spectral clustering with eigenvector selection and self-tuned $k$
https://github.com/mashaan14/PAtree-SGC
공식 코드Graph Construction using Principal Axis Trees for Simple Graph Convolution
https://github.com/mashaan14/RPTree-GCN
공식 코드Random Projection Forest Initialization for Graph Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.