Networks Scalability
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Related Articles from SNS
Semantic and Task-Oriented V2X Communications: Pushing the Limits of V2X Networks Scalability
arXiv:2606.09126v1 Announce Type: new Abstract: Scalable Vehicle-to-Everything (V2X) networks are key to support the large-scale deployment of connected and automated mobility. However, the scalability of V2X networks is currently challenged by the limitations of existing V2X communication paradigms, which prioritize the reliable and timely delivery of the transmitted information over a careful message content selection - an approach that can potentially lead to the transmission of...
Scalable Event Cloud Network for Event-based Classification
arXiv:2412.20803v2 Announce Type: replace Abstract: Event cameras are biologically inspired sensors garnering significant attention from both industry and academia. Mainstream methods favor frame and voxel representations, which reach a satisfactory performance while introducing time-consuming transformations, bulky models, and sacrificing fine-grained temporal information. Alternatively, Point Cloud representation demonstrates promise in addressing the mentioned weaknesses, but it has...
A Communication-Centric 6G-LLM Architecture for Scalable Tactical Autonomous Defense Vehicle Networks
Announce Type: cross Abstract: The integration of Artificial Intelligence (AI) and emerging 6G networks introduces new opportunities for scalable coordination in tactical autonomous vehicle systems. This paper proposes a communication-centric hierarchical architecture for Tactical Autonomous Defense Vehicle Networks (TADVNs) that models the integration of edge-assisted Large Language Model (LLM) reasoning with 6G-enabled connectivity and semantic communication. The framework is designed to...
R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks
arXiv:2504.01250v2 Announce Type: replace Abstract: This paper presents the Robust Recurrent Deep Network (R2DN), a scalable parameterization of robust recurrent neural networks for machine learning and data-driven control. We construct R2DNs as the feedback interconnection of a linear time-invariant system and a 1-Lipschitz deep feedforward network, and directly parameterize the weights so that our models are stable (contracting) and robust to small input perturbations (Lipschitz) by...
Optimizing Energy-based Neural Network Training with Coherent Ising Machine
Announce Type: new Abstract: While Ising machines serve as advanced physical solvers for the Ising model,enabling applications in combinatorial optimization and neural network training,their scalability for large-scale neural networks remains constrained by hardware connectivity limitations and suboptimal training methodologies. In this work,we leverage a Coherent Ising Machine (CIM) to train an energy-based neural network using Equilibrium Propagation, achieving performance comparable to...
Admittance Sensitivity-Informed Modular GP for Scalable Topology-Adaptive Power-Flow Learning
arXiv:2606.03717v1 Announce Type: new Abstract: Data-driven approaches for learning power flow models suffer from weak generalization across varying network topologies and limited computational scalability. Existing methods typically rely on training over a large set of grid topologies, which becomes impractical for large networks. This paper proposes a scalable and computationally efficient framework for topology-adaptive learning of power flow solutions.
Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation
Announce Type: cross Abstract: Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circuit evaluations that grows quadratically with the number of trainable parameters, making hardware-based optimisation impractical beyond small system sizes. In this work, we introduce a training framework that reduces this cost to logarithmic in the number of qubits, making...
A Unified Framework for Locality in Scalable MARL
arXiv:2602.16966v2 Announce Type: replace Abstract: Scalable methods for networked multi-agent reinforcement learning let each agent plan using only a small neighborhood of the agent graph. This works only when the system is value-local, meaning a perturbation at one agent affects the long-run value at another agent weakly when the two are far apart. In the average-reward setting, the standard way to certify locality is the Dobrushin row-sum bound on a single matrix $C^\pi$ that captures how...
Scalable GANs with Transformers
Announce Type: replace Abstract: Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning. We investigate the scalability of Generative Adversarial Networks (GANs) through two design choices that have proven to be effective in other types of generative models: training in a compact Variational Autoencoder latent space and adopting purely transformer-based generators and discriminators. Training in latent space enables...
Enhancing AI Interpretability and Safety through Localised Architectures
arXiv:2606.07998v1 Announce Type: new Abstract: Recent advances in generative AI, especially powerful Large Language Models (LLMs) and Large Reasoning Models (LRMs), raise concerns over the interpretability, safety and sustainability of these large and opaque AI models. The power of such architectures is derived not only from the scalability of deep neural networks, but also massively parallel hardware such as GPU clusters.