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Graph Neural Networks Pretraining Through Inherent Supervision for Molecular Property Prediction. 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Collaborative Image Understanding. 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DocSemMap 2.0: Semantic Labeling based on Textual Data Documentations Using Seq2Seq Context Learner. 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Memory Graph with Message Rehearsal for Multi-Turn Dialogue Generation. 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Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data. 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A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery. 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AutoXAI: A Framework to Automatically Select the Most Adapted XAI Solution. 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Consistent, Balanced, and Overlapping Label Trees for Extreme Multi-label Learning. 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POTATO: exPlainable infOrmation exTrAcTion framewOrk. 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Samba: Identifying Inappropriate Videos for Young Children on YouTube. 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Mining of Real-world Hypergraphs: Patterns, Tools, and Generators. 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Mining of Real-world Hypergraphs: Patterns, Tools, and Generators. 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Predicting Multi-level Socioeconomic Indicators from Structural Urban Imagery. 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Dynamic Network Embedding via Temporal Path Adjacency Matrix Factorization. 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Sampling Enclosing Subgraphs for Link Prediction. 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Scalable Multiple Kernel k-means Clustering. 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Self-Paced and Discrete Multiple Kernel k-Means. 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DEMO: Disentangled Molecular Graph Generation via an Invertible Flow Model. 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FedCDR: Federated Cross-Domain Recommendation for Privacy-Preserving Rating Prediction. 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Music4All-Onion - A Large-Scale Multi-Faceted Content-Centric Music Recommendation Dataset. 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Implicit Session Contexts for Next-Item Recommendations. 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Rank List Sensitivity of Recommender Systems to Interaction Perturbations. 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Plotly.plus, an Improved Dataset for Visualization Recommendation. 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MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy. 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ST-GAT: A Spatio-Temporal Graph Attention Network for Accurate Traffic Speed Prediction. 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RelpNet: Relation-based Link Prediction Neural Network. 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Leveraging Multiple Types of Domain Knowledge for Safe and Effective Drug Recommendation. 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DuARUS: Automatic Geo-object Change Detection with Street-view Imagery for Updating Road Database at Baidu Maps. 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Effects of Stubbornness on Opinion Dynamics. 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Tutorial on Deep Learning Interpretation: A Data Perspective. 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Dismantling Complex Networks by a Neural Model Trained from Tiny Networks. 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RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation. 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End-to-end Modularity-based Community Co-partition in Bipartite Networks. 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Multi-task Learning with Adaptive Global Temporal Structure for Predicting Alzheimer's Disease Progression. 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SEERa: A Framework for Community Prediction. 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Proceedings of the 31st ACM International Conference on Information & Knowledge Management. (2022) ISBN:9781450392365<div class="Book Publication long-list"> <div class="authors"> <div class="autype autype0"> <span class="author-affil">ACM</span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;33168281" target="_blank">Proceedings of the 31st ACM International Conference on Information & Knowledge Management</a></div><div class="conference"> Konferencia helye, ideje: <span class="location">Atlanta, Amerikai Egyesült Államok <span class="conference-date">2022.10.17. - 2022.10.21.</span> </div><div class="publish-data"> <span class="published-at"> New York</span>: <span class="publishers">Association for Computing Machinery (ACM)</span>, <span class="year">(2022)</span> </div> <span class="volume"> <span class="volumeTitle"></span> </span> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:black" title="10.1145/3511808" target="_blank" href="https://doi.org/10.1145/3511808"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <span class="isbnOrIssn"> ISBN: </span> <a style="color:black" title="9781450392365" target="_blank" href="https://www.worldcat.org/search?q=isbn%3A9781450392365"> 9781450392365 </a> </span> </span> <div class="mtid"><span class="long-pub-mtid">Közlemény: 33168281</span> | <span class="status-data status-APPROVED"> Nyilvános </span> | <span class="type-subtype">Könyv ( Konferenciakötet ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">Scopus</span> </div> <div class="lastModified">Utolsó módosítás: 2023.02.10. 12:05 Nagy Eszter (SZTA admi, admin) </div> <div class="chapters"> <b><a target="_blank" href="/api/publication?from=BookChapter&cond=book.mtid;eq;33168281">Fejezetek: 37</a></b> </div> </div></div>