Awesome Signal Processing

Key papers

• Finsler Geometry, Graph Neural Networks, and You (2026)
  T. Mitchell Roddenberry et al.
  4.39
• Deep Learning-Based Channel Extrapolation for Dual-Band Massive MIMO Systems (2026)
  Qikai Xiao et al.
  2.00
• Single-Snapshot Gridless 2D-DoA Estimation for UCAs: A Joint Optimization Approach (2025)
  Salar Nouri
  1.56
• GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations (2025)
  Milad Ramezankhani et al.
  1.33
• Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach (2025)
  Hao Fang et al.
  1.28
• Using Subgraph GNNs for Node Classification:an Overlooked Potential Approach (2025)
  Qian Zeng et al.
  1.17
• RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains (2025)
  Sepehr Mousavi et al.
  1.06
• Doubly Nested Network for Resource-Efficient Inference (2018)
  Jaehong Kim et al.
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• Fast and Accurate Network Embeddings via Very Sparse Random Projection (2019)
  Haochen Chen et al.
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• Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding (2019)
  Yun Tang et al.
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• Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning (2019)
  Xiang Ni et al.
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• Gradient Coding with Dynamic Clustering for Straggler Mitigation (2020)
  Baturalp Buyukates and Emre Ozfatura and Sennur Ulukus and Deniz Gunduz
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• MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning (2020)
  Sen Lin et al.
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• Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning (2020)
  Fernando Gama et al.
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• Graph-based Neural Architecture Search with Operation Embeddings (2021)
  Michail Chatzianastasis et al.
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• A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement (2021)
  Jose Jurandir Alves Esteves et al.
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• Efficient and Reliable Overlay Networks for Decentralized Federated Learning (2021)
  Yifan Hua et al.
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• Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach (2022)
  Davide Buffelli et al.
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• DNNFuser: Generative Pre-Trained Transformer as a Generalized Mapper for Layer Fusion in DNN Accelerators (2022)
  Sheng-Chun Kao et al.
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• Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences (2022)
  Gordon Euhyun Moon et al.
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• Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks (2022)
  Ziyang Zheng et al.
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• Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data (2022)
  Jiajin Li et al.
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• Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm (2022)
  Meng Liu et al.
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• CSGO: Constrained-Softassign Gradient Optimization For Large Graph Matching (2022)
  Binrui Shen et al.
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• Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN (2022)
  Yadi Cao et al.
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• New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction (2022)
  Guillaume Salha-Galvan and Johannes F. Lutzeyer and George Dasoulas and Romain Hennequin and Michalis Vazirgiannis
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• From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm (2022)
  Jie Chen et al.
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• Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning (2023)
  Marco Skocaj et al.
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• Flexible Channel Dimensions for Differentiable Architecture Search (2023)
  Ahmet Caner Y\"uz\"ug\"uler and Nikolaos Dimitriadis and Pascal Frossard
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• DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion (2023)
  Jining Wang et al.
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• Robust Networked Federated Learning for Localization (2023)
  Reza Mirzaeifard et al.
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• T-GAE: Transferable Graph Autoencoder for Network Alignment (2023)
  Jiashu He et al.
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• Robust Graph Matching Using An Unbalanced Hierarchical Optimal Transport Framework (2023)
  Haoran Cheng et al.
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• MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy (2023)
  Haitian Jiang et al.
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• Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity (2023)
  Lorenzo Valerio et al.
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• Distributed Pose-graph Optimization with Multi-level Partitioning for Collaborative SLAM (2024)
  Cunhao Li et al.
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• Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling (2024)
  Yuhong Luo and Pan Li
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• DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning of Gough-Stewart Platform (2024)
  Huizhi Zhu et al.
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• Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction (2024)
  Junhua Liu et al.
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• Learning-to-solve unit commitment based on few-shot physics-guided spatial-temporal graph convolution network (2024)
  Mei Yang and Gao Qiu andJunyong Liu and Kai Liu
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• GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications (2024)
  Ois\'in M. Morrison et al.
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• Design and Optimization of Hierarchical Gradient Coding for Distributed Learning at Edge Devices (2024)
  Weiheng Tang and Jingyi Li and Lin Chen and Xu Chen
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• Efficient Graph Similarity Computation with Alignment Regularization (2024)
  Wei Zhuo et al.
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• Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks (2024)
  Yudi Huang et al.
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• Efficient Network Embedding by Approximate Equitable Partitions (2024)
  Giuseppe Squillace et al.
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• Graph Retention Networks for Dynamic Graphs (2024)
  Qian Chang et al.
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• DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models (2024)
  Haonan Yuan et al.
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