Learning Connectivity-Maximizing Network Configurations

• URL: http://arxiv.org/abs/2112.07663v1
• Date: Tue, 14 Dec 2021 18:59:01 GMT
• Title: Learning Connectivity-Maximizing Network Configurations
• Authors: Daniel Mox, Vijay Kumar, Alejandro Ribeiro
• Abstract summary: We propose a supervised learning approach with a convolutional neural network (CNN) that learns to place communication agents from an expert. We demonstrate the performance of our CNN on canonical line and ring topologies, 105k randomly generated test cases, and larger teams not seen during training. After training, our system produces connected configurations 2 orders of magnitude faster than the optimization-based scheme for teams of 10-20 agents.
• Score: 123.01665966032014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work we propose a data-driven approach to optimizing the algebraic connectivity of a team of robots. While a considerable amount of research has been devoted to this problem, we lack a method that scales in a manner suitable for online applications for more than a handful of agents. To that end, we propose a supervised learning approach with a convolutional neural network (CNN) that learns to place communication agents from an expert that uses an optimization-based strategy. We demonstrate the performance of our CNN on canonical line and ring topologies, 105k randomly generated test cases, and larger teams not seen during training. We also show how our system can be applied to dynamic robot teams through a Unity-based simulation. After training, our system produces connected configurations 2 orders of magnitude faster than the optimization-based scheme for teams of 10-20 agents.

Related papers

• FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
  Decentralized training faces significant challenges regarding system design and efficiency. We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs) We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
  arXiv  Detail & Related papers  (2024-10-16T16:13:19Z)
• Training Spiking Neural Networks with Local Tandem Learning [96.32026780517097]
  Spiking neural networks (SNNs) are shown to be more biologically plausible and energy efficient than their predecessors. In this paper, we put forward a generalized learning rule, termed Local Tandem Learning (LTL) We demonstrate rapid network convergence within five training epochs on the CIFAR-10 dataset while having low computational complexity.
  arXiv  Detail & Related papers  (2022-10-10T10:05:00Z)
• FPGA-based AI Smart NICs for Scalable Distributed AI Training Systems [62.20308752994373]
  We propose a new smart network interface card (NIC) for distributed AI training systems using field-programmable gate arrays (FPGAs) Our proposed FPGA-based AI smart NIC enhances overall training performance by 1.6x at 6 nodes, with an estimated 2.5x performance improvement at 32 nodes, compared to the baseline system using conventional NICs.
  arXiv  Detail & Related papers  (2022-04-22T21:57:00Z)
• Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks [3.7630209350186807]
  Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency. Deep Learning inference orchestration strategy employs reinforcement learning to find the optimal orchestration policy. We demonstrate efficacy of our HL strategy through experimental comparison with state-of-the-art RL-based inference orchestration.
  arXiv  Detail & Related papers  (2022-02-21T21:50:50Z)
• Coordinated Reinforcement Learning for Optimizing Mobile Networks [6.924083445159127]
  We show how to use coordination graphs and reinforcement learning in a complex application involving hundreds of cooperating agents. We show empirically that coordinated reinforcement learning outperforms other methods.
  arXiv  Detail & Related papers  (2021-09-30T14:46:18Z)
• Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection [101.38634057635373]
  We focus on applications where robots are required to jointly select actions to maximize team submodular objectives. We propose a general-purpose learning architecture towards submodular at scale, with decentralized communications. We demonstrate the performance of our GNN-based learning approach in a scenario of active target coverage with large networks of robots.
  arXiv  Detail & Related papers  (2021-05-18T15:32:07Z)
• Optimizing Neural Networks via Koopman Operator Theory [6.09170287691728]
  Koopman operator theory was recently shown to be intimately connected with neural network theory. In this work we take the first steps in making use of this connection. We show that Koopman operator theory methods allow predictions of weights and biases of feed weights over a non-trivial range of training time.
  arXiv  Detail & Related papers  (2020-06-03T16:23:07Z)
• Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment [84.57874289554839]
  Training deep neural networks on large-scale datasets requires significant hardware resources. Backpropagation, the workhorse for training these networks, is an inherently sequential process that is difficult to parallelize. We propose a neuro-biologically-plausible alternative to backprop that can be used to train deep networks.
  arXiv  Detail & Related papers  (2020-02-10T16:20:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.