https://github.com/1041877801/COMBHelper
공식 코드COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Ahmadreza401/fair_influen_max
공식 코드Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks
https://github.com/Arhosseini77/Brand_Attention
공식 코드https://github.com/Arhosseini77/SUM
공식 코드SUM: Saliency Unification through Mamba for Visual Attention Modeling
https://github.com/AyanKumarBhunia/Deep-One-Shot-Logo-Retrieval
공식 코드https://github.com/BokwaiHo/revenue_uplift
공식 코드Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing
https://github.com/DavidHason/Causal_Analysis
공식 코드https://github.com/DavyMorgan/ICDE20-PUP
공식 코드Price-aware Recommendation with Graph Convolutional Networks
https://github.com/DepartmentOfStatisticsPUE/rid-gig-economy
공식 코드The gig economy in Poland: evidence based on mobile big data
https://github.com/GhanshyamVerma/Explainable-Recommender-System
공식 코드https://github.com/Humboldt-WI/response-dependent-costs
공식 코드https://github.com/Kalashi-Saed-Collaborations/SentimentDrivenCommunityDetection
공식 코드Sentiment-Driven Community Detection in a Network of Perfume Preferences
https://github.com/LCS2-IIITD/LUMEN-Explaining-Memes
공식 코드What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes
https://github.com/LiangQiao94/MH-STM
공식 코드https://github.com/MKaptein/ofmlr
공식 코드https://github.com/Mayimunah/Affinity-progation-sentiment-analysis
공식 코드Enhancing Affinity Propagation for Improved Public Sentiment Insights
https://github.com/MengtingWan/marketBias
공식 코드https://github.com/Networks-Learning/tpprl
공식 코드Deep Reinforcement Learning of Marked Temporal Point Processes
https://github.com/Priyankagautam08/DynamicGraphInfluenceMaximization-AAAI2025
공식 코드GNN-Based Candidate Node Predictor for Influence Maximization in Temporal Graphs
https://github.com/WeilinChen507/LASER
공식 코드Long-term Causal Effects Estimation via Latent Surrogates Representation Learning
https://github.com/YipingNUS/slogan-generation-dataset
공식 코드Toward Improving Coherence and Diversity of Slogan Generation
https://github.com/adeeph/dravidian-oli
공식 코드https://github.com/aiueola/webconf2024-slate-ope-via-abstraction
공식 코드Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction
https://github.com/alessiomuscillo/modeling_waves
공식 코드https://github.com/alibaba/hybridbackend
공식 코드PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems
https://github.com/apairofbigwings/cross-lingual-opinion-mining
공식 코드A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining
https://github.com/aridhasan/mubase
공식 코드https://github.com/armintabari/Emotion-Detection-RNN
공식 코드Emotion Detection in Text: Focusing on Latent Representation
https://github.com/asopi/rental-project
공식 코드Implementing Smart Contracts: The case of NFT-rental with pay-per-like
https://github.com/banking-analytics-lab/dynamicgraphlearning
공식 코드https://github.com/banking-analytics-lab/inflect
공식 코드INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks
https://github.com/beyzabozdag/PersuasionSurvey
공식 코드https://github.com/bokelmab/uplift_metrics
공식 코드https://github.com/bookingcom/uplift-interference-simulator
공식 코드https://github.com/boschresearch/local_neural_transformations
공식 코드Detecting Anomalies within Time Series using Local Neural Transformations
https://github.com/brkljac/personReID
공식 코드Person detection and re-identification in open-world settings of retail stores and public spaces
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.