Switch-Based Multi-Part Neural Network

• URL: http://arxiv.org/abs/2504.18241v1
• Date: Fri, 25 Apr 2025 10:39:42 GMT
• Title: Switch-Based Multi-Part Neural Network
• Authors: Surajit Majumder, Paritosh Ranjan, Prodip Roy, Bhuban Padhan,
• Abstract summary: Decentralized and modular neural network framework designed to enhance the scalability, interpretability, and performance of AI systems.<n>At the heart of this framework is a dynamic switch mechanism that governs the selective activation and training of individual neurons.
• Score: 0.15749416770494706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces decentralized and modular neural network framework designed to enhance the scalability, interpretability, and performance of artificial intelligence (AI) systems. At the heart of this framework is a dynamic switch mechanism that governs the selective activation and training of individual neurons based on input characteristics, allowing neurons to specialize in distinct segments of the data domain. This approach enables neurons to learn from disjoint subsets of data, mimicking biological brain function by promoting task specialization and improving the interpretability of neural network behavior. Furthermore, the paper explores the application of federated learning and decentralized training for real-world AI deployments, particularly in edge computing and distributed environments. By simulating localized training on non-overlapping data subsets, we demonstrate how modular networks can be efficiently trained and evaluated. The proposed framework also addresses scalability, enabling AI systems to handle large datasets and distributed processing while preserving model transparency and interpretability. Finally, we discuss the potential of this approach in advancing the design of scalable, privacy-preserving, and efficient AI systems for diverse applications.

Related papers

• Neural network interpretability with layer-wise relevance propagation: novel techniques for neuron selection and visualization [0.49478969093606673]
  We present a novel approach that improves the parsing of selected neurons during.<n>LRP backward propagation, using the Visual Geometry Group 16 (VGG16) architecture as a case study.<n>Our approach enhances interpretability and supports the development of more transparent artificial intelligence (AI) systems for computer vision applications.
  arXiv  Detail & Related papers  (2024-12-07T15:49:14Z)
• Peer-to-Peer Learning Dynamics of Wide Neural Networks [10.179711440042123]
  We provide an explicit characterization of the learning dynamics of wide neural networks trained using distributed gradient descent (DGD) algorithms.<n>Our results leverage both recent in neural tangent kernel (NTK) theory and extensive previous work on distributed learning and consensus tasks.
  arXiv  Detail & Related papers  (2024-09-23T17:57:58Z)
• Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
  We propose a biologically-informed framework for enhancing artificial neural networks (ANNs) Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors. We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• Coding schemes in neural networks learning classification tasks [52.22978725954347]
  We investigate fully-connected, wide neural networks learning classification tasks. We show that the networks acquire strong, data-dependent features. Surprisingly, the nature of the internal representations depends crucially on the neuronal nonlinearity.
  arXiv  Detail & Related papers  (2024-06-24T14:50:05Z)
• Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks [0.0]
  We introduce and evaluate a brain-like neural network model capable of unsupervised representation learning.<n>The model was tested on a diverse set of popular machine learning benchmarks.
  arXiv  Detail & Related papers  (2024-06-07T08:32:30Z)
• Initialisation and Network Effects in Decentralised Federated Learning [1.5961625979922607]
  Decentralised federated learning enables collaborative training of individual machine learning models on a distributed network of communicating devices. This approach avoids central coordination, enhances data privacy and eliminates the risk of a single point of failure. We propose a strategy for uncoordinated initialisation of the artificial neural networks based on the distribution of eigenvector centralities of the underlying communication network.
  arXiv  Detail & Related papers  (2024-03-23T14:24:36Z)
• Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
  We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations. Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
  arXiv  Detail & Related papers  (2024-02-20T15:23:24Z)
• A Transition System Abstraction Framework for Neural Network Dynamical System Models [2.414910571475855]
  This paper proposes a transition system abstraction framework for neural network dynamical system models. The framework is able to abstract a data-driven neural network model into a transition system, making the neural network model interpretable.
  arXiv  Detail & Related papers  (2024-02-18T23:49:18Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Inducing Gaussian Process Networks [80.40892394020797]
  We propose inducing Gaussian process networks (IGN), a simple framework for simultaneously learning the feature space as well as the inducing points. The inducing points, in particular, are learned directly in the feature space, enabling a seamless representation of complex structured domains. We report on experimental results for real-world data sets showing that IGNs provide significant advances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-21T05:27:09Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• FF-NSL: Feed-Forward Neural-Symbolic Learner [70.978007919101]
  This paper introduces a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FF-NSL) FF-NSL integrates state-of-the-art ILP systems based on the Answer Set semantics, with neural networks, in order to learn interpretable hypotheses from labelled unstructured data.
  arXiv  Detail & Related papers  (2021-06-24T15:38:34Z)
• AI Centered on Scene Fitting and Dynamic Cognitive Network [4.228224431041357]
  This paper briefly analyzes the advantages and problems of AI mainstream technology and puts forward: To achieve stronger Artificial Intelligence, the end-to-end function calculation must be changed. It also discusses the concrete scheme named Dynamic Cognitive Network model (DC Net)
  arXiv  Detail & Related papers  (2020-10-02T06:13:41Z)

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.