KDD2026-LATTE:Learning AdaptiveSegmentation for Efficient and EffectiveTrajectory SimilarityLearning @TheOfficialACM
KDD2026-LATTE:Learning AdaptiveSegmentation for Efficient and EffectiveTrajectory SimilarityLearning @TheOfficialACM
Uploaded July 2026 | Updated September 2026, 2 weeks ago
Jialiang Li, Hua Lu, Tiantian Liu, Zhichen Lai, Pengfei Li
KDD2026-LATTE:Learning AdaptiveSegmentation for Efficient and EffectiveTrajectory SimilarityLearningKDD 2026 - Out-of-Distribution Robust Explainer for Graph Neural NetworksKDD 2026 - Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?KDD2026-SciHorizon-DataEVA:AnAgenticSys. to ScalableAI-Readiness Eval. of Heterogeneous Scient. DataPeople of ACM: Russ Cox (8/11/2026)KDD 2026-CORF-Net: Cross-Order Representation Fusion Network for Denoised Survey Response ModelingKDD2026-Deformation Local. TheoryGuided TransferLearning for Mining-inducedSeism.Risk ZonePredictionKDD 2026 - ARC-TGI: Human-Validated Task Generators with Reasoning Chain Templates for ARC-AGIKDD2026-DeepTaxon:AnInterpretableRetrieval-AugmentedMultimodal Framework forUni.SpeciesIdent.andDis.KDD 2026-HDMoE:A HierarcalDecoupling-FusionMixture-of-Experts FrameworkforMultim.CancerSurvivalPred.KDD 2026 - MFC: Mixed Federated Clustering based on Cross-modal Feature Decoupling
Association for Computing Machinery (ACM) |

KDD2026-LATTE:Learning AdaptiveSegmentation for Efficient and EffectiveTrajectory SimilarityLearning

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