https://github.com/sunanhe/meddr
공식 코드GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
Papers with Code 아카이브
Distributed Computing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/suquark/hoplite
공식 코드Hoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed Systems
https://github.com/tyuxie/PhyloVAE
공식 코드PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders
https://github.com/um-dsp/PRICURE
공식 코드PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting
https://github.com/vectorinstitute/flex_model
공식 코드FlexModel: A Framework for Interpretability of Distributed Large Language Models
https://github.com/www2022paper/www-2022-paper-supplementary-materials
공식 코드LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm
https://github.com/xiasong0501/cem
공식 코드https://github.com/yawen-shao/great_code
공식 코드GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding
https://github.com/yigitokar/IDMR
공식 코드https://github.com/yuanmu97/mlink
공식 코드MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference
https://github.com/zhanglabtools/dbmd
공식 코드Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering
https://github.com/zhanglabtools/dbmd-dev
공식 코드Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering
https://gitlab.com/transalg/transalg
공식 코드Encoding Cryptographic Functions to SAT Using Transalg System
https://gitlab.com/vchariso/distributed-eigenspace-estimation
공식 코드https://github.com/2023-MindSpore-4/Code13/tree/main/zhanglan/MLink-main
MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference
https://github.com/2023-MindSpore-4/Code13/tree/main/zhanglan/MLink-main/mlink_ms
MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference
https://github.com/Alcoholrithm/TabularS3L
SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning
https://github.com/Aratz/orchestral
Orchestral: a lightweight framework for parallel simulations of cell-cell communication
https://github.com/Automunge/AutoMunge
Optuna: A Next-generation Hyperparameter Optimization Framework
https://github.com/Farhad-n/MultiGPU_Study
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study
https://github.com/Joshua-Ogbebor/hyper_param-optim
https://github.com/JunweiLiang/DualAttentionNetwork
Dual Attention Networks for Multimodal Reasoning and Matching
https://github.com/MindSpore-scientific-2/code-4/tree/main/Corella
Private Collaborative Edge Inference via Over-the-Air Computation
https://github.com/MindSpore-scientific-2/code-5/tree/main/Corella
Private Collaborative Edge Inference via Over-the-Air Computation
https://github.com/MindSpore-scientific-2/code-9/tree/main/INR-Implicit-Neural-Representations-with-Periodic-Activation-Functions
Residual-INR: Communication Efficient On-Device Learning Using Implicit Neural Representation
https://github.com/MindSpore-scientific/code-12/tree/main/INR-Implicit-Neural-Representations-with-Periodic-Activation-Functions
Residual-INR: Communication Efficient On-Device Learning Using Implicit Neural Representation
https://github.com/anathan90/RADiSA
Optimization for Large-Scale Machine Learning with Distributed Features and Observations
https://github.com/astrazeneca/subtab
SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning
https://github.com/awslabs/syne-tune
https://github.com/benedekrozemberczki/karateclub
Billion-scale Network Embedding with Iterative Random Projection
https://github.com/bigscience-workshop/petals
Petals: Collaborative Inference and Fine-tuning of Large Models
https://github.com/brethvoice/optuna_demo_MNIST
Optuna: A Next-generation Hyperparameter Optimization Framework
https://github.com/c-bata/goptuna
https://github.com/crcrpar/benchmark-runner-ci
Optuna: A Next-generation Hyperparameter Optimization Framework
https://github.com/crcrpar/ci-example-execution
Optuna: A Next-generation Hyperparameter Optimization Framework
https://github.com/crcrpar/optuna-mirror
Optuna: A Next-generation Hyperparameter Optimization Framework
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.