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https://arxiv.org/abs/2310.11292v1 [2310.11292v1] Two subspace methods for frequency sparse graph signals Abstract page for arXiv paper 2310.11292v1: Two subspace methods for frequency sparse graph signals sparse graph 2310 two subspace methods https://deepai.org/publication/sgcn-sparse-graph-convolution-network-for-pedestrian-trajectory-prediction SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction | DeepAI Apr 4, 2021 - 04/04/21 - Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very challenging due to complex interaction... sparse graph convolution network pedestrian trajectory https://arxiv.org/abs/2405.10649 [2405.10649] Efficient Recovery of Sparse Graph Signals from Graph Filter Outputs Abstract page for arXiv paper 2405.10649: Efficient Recovery of Sparse Graph Signals from Graph Filter Outputs sparse graph 2405 10649 efficient recovery https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-0/mkl-graph-vector-set-sparse.html mkl_graph_vector_set_sparse Updates a graph vector with the data in a sparse format. mkl graph vector set sparse https://arxiv.org/html/2409.15736v1 SoMaSLAM: 2D Graph SLAM for Sparse Range Sensing with Soft Manhattan World Constraints https://arxiv.org/abs/2207.08629v2 [2207.08629v2] Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks Abstract page for arXiv paper 2207.08629v2: Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks https://openreview.net/forum?id=aUX5Plaq7Oy Learning continuous-time PDEs from sparse data with graph neural networks | OpenReview The behavior of many dynamical systems follow complex, yet still unknown partial differential equations (PDEs). While several machine learning methods have... graph neural networks continuous time https://jmlr.org/papers/v22/19-944.html Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation laplacian matrix learning graph signals sparse https://deepai.org/publication/svga-net-sparse-voxel-graph-attention-network-for-3d-object-detection-from-point-clouds SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds | DeepAI Jun 7, 2020 - 06/07/20 - Accurate 3D object detection from point clouds has become a crucial component in autonomous driving. However, the volumetric repre... https://arxiv.org/abs/1006.1117 [1006.1117] On the hardness of distance oracle for sparse graph Abstract page for arXiv paper 1006.1117: On the hardness of distance oracle for sparse graph on the distance oracle 1006 1117 hardness https://openreview.net/forum?id=K7cAyMYXJR&referrer=%5Bthe%20profile%20of%20Ziwei%20Shi%5D(%2Fprofile%3Fid%3D~Ziwei_Shi1) SGA-Net: A Sparse Graph Attention Network for Two-View Correspondence Learning | OpenReview Establishing reliable correspondences between two images is a fundamental and important task in computer vision. This paper proposes a novel network called... graph attention network https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-1/mkl-graph-vector-set-sparse.html mkl_graph_vector_set_sparse Updates a graph vector with the data in a sparse format. mkl graph vector set sparse https://aclanthology.org/2025.ccl-1.72/ BiSaGA: A Novel Bidirectional Sparse Graph Attention Adapter for Evidence-Based Fact-Checking - ACL... Junfeng Ran, Weiyao Luo, Zailong Tian, Guangxiang Zhao, Dawei Zhu, Longyun Wu, Hailiang Huang, Sujian Li. Proceedings of the 24th China National Conference on... https://openreview.net/forum?id=mnRLzeNsVN&referrer=%5Bthe%20profile%20of%20Balazs%20Kulcsar%5D(%2Fprofile%3Fid%3D~Balazs_Kulcsar1) Travelling Salesman Problem Goes Sparse With Graph Neural Networks | OpenReview Machine learning based approaches to solve the Travelling Salesman Problem (TSP) have achieved astonishing performance in the last years. A large number of... travelling salesman problem graph neural networks goes sparse openreview https://openreview.net/forum?id=OIvg3MqWX2 A Theoretically-Principled Sparse, Connected, and Rigid Graph Representation of Molecules |... Graph neural networks (GNNs) -- learn graph representations by exploiting the graph's sparsity, connectivity, and symmetries -- have become indispensable for... rigid graph theoretically principled sparse connected https://deepai.org/publication/uncertainty-quantification-of-sparse-travel-demand-prediction-with-spatial-temporal-graph-neural-networks Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural... Aug 11, 2022 - 08/11/22 - Origin-Destination (O-D) travel demand prediction is a fundamental challenge in transportation. Recently, spatial-temporal deep le... uncertainty quantification travel demand