Awesome Signal Processing
Signal Processing is one of the most active areas in Awesome AI Agents — 47 papers in this collection. A strong starting point is "Finsler Geometry, Graph Neural Networks, and You".
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 Nouri1.56
- 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.—
- Fast and Accurate Network Embeddings via Very Sparse Random Projection (2019)Haochen Chen et al.—
- Orthogonal Relation Transforms with Graph Context Modeling for Knowledge
Graph Embedding (2019)Yun Tang et al.—
- Generalizable Resource Allocation in Stream Processing via Deep
Reinforcement Learning (2019)Xiang Ni et al.—
- Gradient Coding with Dynamic Clustering for Straggler Mitigation (2020)Baturalp Buyukates and Emre Ozfatura and Sennur Ulukus and Deniz Gunduz—
- MetaGater: Fast Learning of Conditional Channel Gated Networks via
Federated Meta-Learning (2020)Sen Lin et al.—
- Synthesizing Decentralized Controllers with Graph Neural Networks and
Imitation Learning (2020)Fernando Gama et al.—
- Graph-based Neural Architecture Search with Operation Embeddings (2021)Michail Chatzianastasis et al.—
- A Heuristically Assisted Deep Reinforcement Learning Approach for
Network Slice Placement (2021)Jose Jurandir Alves Esteves et al.—
- Efficient and Reliable Overlay Networks for Decentralized Federated
Learning (2021)Yifan Hua et al.—
- Graph Representation Learning for Multi-Task Settings: a Meta-Learning
Approach (2022)Davide Buffelli et al.—
- DNNFuser: Generative Pre-Trained Transformer as a Generalized Mapper for
Layer Fusion in DNN Accelerators (2022)Sheng-Chun Kao et al.—
- Parallel Training of GRU Networks with a Multi-Grid Solver for Long
Sequences (2022)Gordon Euhyun Moon et al.—
- Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form
Deep Neural Networks (2022)Ziyang Zheng et al.—
- Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph
Data (2022)Jiajin Li et al.—
- Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm (2022)Meng Liu et al.—
- CSGO: Constrained-Softassign Gradient Optimization For Large Graph
Matching (2022)Binrui Shen et al.—
- Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN (2022)Yadi Cao et al.—
- 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—
- From Node Interaction to Hop Interaction: New Effective and Scalable
Graph Learning Paradigm (2022)Jie Chen et al.—
- Uplink Scheduling in Federated Learning: an Importance-Aware Approach
via Graph Representation Learning (2023)Marco Skocaj et al.—
- Flexible Channel Dimensions for Differentiable Architecture Search (2023)Ahmet Caner Y\"uz\"ug\"uler and Nikolaos Dimitriadis and Pascal Frossard—
- DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph
Completion (2023)Jining Wang et al.—
- Robust Networked Federated Learning for Localization (2023)Reza Mirzaeifard et al.—
- T-GAE: Transferable Graph Autoencoder for Network Alignment (2023)Jiashu He et al.—
- Robust Graph Matching Using An Unbalanced Hierarchical Optimal Transport
Framework (2023)Haoran Cheng et al.—
- MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a
Sampling-Based Energy (2023)Haitian Jiang et al.—
- Coordination-free Decentralised Federated Learning on Complex Networks:
Overcoming Heterogeneity (2023)Lorenzo Valerio et al.—
- Distributed Pose-graph Optimization with Multi-level Partitioning for
Collaborative SLAM (2024)Cunhao Li et al.—
- Scalable and Efficient Temporal Graph Representation Learning via
Forward Recent Sampling (2024)Yuhong Luo and Pan Li—
- DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning
of Gough-Stewart Platform (2024)Huizhi Zhu et al.—
- Spatial-Temporal Graph Representation Learning for Tactical Networks
Future State Prediction (2024)Junhua Liu et al.—
- 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—
- GFN: A graph feedforward network for resolution-invariant reduced
operator learning in multifidelity applications (2024)Ois\'in M. Morrison et al.—
- 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—
- Efficient Graph Similarity Computation with Alignment Regularization (2024)Wei Zhuo et al.—
- Overlay-based Decentralized Federated Learning in Bandwidth-limited
Networks (2024)Yudi Huang et al.—
- Efficient Network Embedding by Approximate Equitable Partitions (2024)Giuseppe Squillace et al.—
- Graph Retention Networks for Dynamic Graphs (2024)Qian Chang et al.—
- DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with
Selective State Space Models (2024)Haonan Yuan et al.—