KDD2026-DSPR: Dual-StreamPhysics-Residual Networks for Trustworthy Industrial Time SeriesForecasting @TheOfficialACM
KDD2026-DSPR: Dual-StreamPhysics-Residual Networks for Trustworthy Industrial Time SeriesForecasting @TheOfficialACM
Uploaded July 2026 | Updated September 2026, 2 weeks ago
Yeran Zhang, Pengwei Yang, Guoqing Wang, Tianyu Li
KDD2026-DSPR: Dual-StreamPhysics-Residual Networks for Trustworthy Industrial Time SeriesForecastingFLASC: Federated LoRA with Sparse CommunicationKDD 2026 - AutoOrbit: Physics-Informed Satellite Orbit PredictionKDD2026-UNITE:A UnifiedFramework for Accu. andEfficientOrigin-Destination andRouteTravelTimeEstim.KDD 2026 - Retrv-MoE: Scaling Unified Multimodal Retrieval with Sparse Mixture-of-ExpertsKDD 2026 - Balanced Multimodal Federated Learning: An Efficient and Noise-Resilient ApproachKDD 2026 - Semi-Supervised Text-Attributed Graph DistillationVinicius Pereira on The Need For Linguistic DiversityKDD 2026-DIREC: Diffusion-Based Review-Embedding Generation for Accurate Cross-Domain RecommendationKDD 2026 - Benchmarking Table Extraction from Heterogeneous Scientific PDF DocumentsKDD2026-LATTE:Learning AdaptiveSegmentation for Efficient and EffectiveTrajectory SimilarityLearningKDD 2026 - Out-of-Distribution Robust Explainer for Graph Neural Networks
Association for Computing Machinery (ACM) |

KDD2026-DSPR: Dual-StreamPhysics-Residual Networks for Trustworthy Industrial Time SeriesForecasting

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