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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zipkinlab/doser_etal_2021_inreview
공식 코드https://github.com/zju-daily/multiem
공식 코드MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching
https://github.com/zjunlp/oneedit
공식 코드OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
https://github.com/zlin7/uq-nlg
공식 코드Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
https://github.com/zsdonghao/tensorlayer
공식 코드TensorLayer: A Versatile Library for Efficient Deep Learning Development
https://github.com/zwd-ms/risk_averse_cb
공식 코드https://github.com/zzbn12345/WHOSe_Heritage
공식 코드https://gitlab.com/ComputationalScience/epidemic-control
공식 코드Controlling epidemics through optimal allocation of test kits and vaccine doses across networks
https://gitlab.com/LungModelling/avatree
공식 코드Patient-specific modelling, simulation and real-time processing for respiratory diseases
https://gitlab.com/anonymousppocl_cm1/anonymous_collisions_paper
공식 코드Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management Problem
https://gitlab.com/bvobart/python-ml-analysis
공식 코드https://gitlab.com/computationalscience/inventory-optimization
공식 코드Control of Dual-Sourcing Inventory Systems using Recurrent Neural Networks
https://gitlab.com/dcasbol/nn-modularization
공식 코드Modularity as a Means for Complexity Management in Neural Networks Learning
https://gitlab.com/picture-production/picture-nnunet-package
공식 코드https://gitlab.com/unidata-labs/ru-wiki-tables-backend
공식 코드Russian Web Tables: A Public Corpus of Web Tables for Russian Language Based on Wikipedia
https://gitlab.com/unidata-labs/ru-wiki-tables-dataset
공식 코드Russian Web Tables: A Public Corpus of Web Tables for Russian Language Based on Wikipedia
https://gitlab.com/unidata-labs/ru-wiki-tables-frontend
공식 코드Russian Web Tables: A Public Corpus of Web Tables for Russian Language Based on Wikipedia
https://gitlab.ifi.uzh.ch/franco/RCVaR
공식 코드RCVaR: an Economic Approach to Estimate Cyberattacks Costs using Data from Industry Reports
https://gitlab.mpi-klsb.mpg.de/yaoyaoliu/rmm
공식 코드RMM: Reinforced Memory Management for Class-Incremental Learning
https://gitlab.mpi-klsb.mpg.de/yaoyaoliu/rmm/
공식 코드RMM: Reinforced Memory Management for Class-Incremental Learning
https://huggingface.co/aminhaeri/risk-embed
공식 코드Generative AI Enhanced Financial Risk Management Information Retrieval
https://zenodo.org/record/6369228
공식 코드Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction
https://bitbucket.org/skeizer/madrigal
Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue Modelling
https://github.com/0xc4f3/dfuc2022_snippets
Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/LEO
LEO Satellite Access Network (LEO-SAN) Towards 6G: Challenges and Approaches
https://github.com/2023-MindSpore-1/ms-code-211/tree/main/delf
https://github.com/2023-MindSpore-4/Code2/tree/main/dcrnn
https://github.com/5410tiffany/EIIE-keras
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/8023looker/med-rr
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
https://github.com/8023looker/versatune
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
https://github.com/ADGEfficiency/dsr_rl
https://github.com/AIwithSwift/TFWorld2019-SwiftIn3Hours
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
https://github.com/AlirezaShamsoshoara/Multi-Independent-Agent-Reinforcement-Learning-UAV-Autonomous-Spectrum-QLearning
https://github.com/AlirezaShamsoshoara/Reinforcement_Learning_Team_Q_learnig_MARL_Multi_Agent_UAV_Spectrum_task
A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks
https://github.com/Allen-Czyysx/Paper-Reading
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.