Transforming to Yoked Neural Networks to Improve ANN Structure

• URL: http://arxiv.org/abs/2306.02157v3
• Date: Thu, 24 Aug 2023 15:51:01 GMT
• Title: Transforming to Yoked Neural Networks to Improve ANN Structure
• Authors: Xinshun Liu and Yizhi Fang and Yichao Jiang
• Abstract summary: Most existing artificial neural networks (ANN) are designed as a tree structure to imitate neural networks. We propose a model YNN to efficiently eliminate such structural bias. In our model, nodes also carry out aggregation and transformation of features, and edges determine the flow of information.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Most existing classical artificial neural networks (ANN) are designed as a tree structure to imitate neural networks. In this paper, we argue that the connectivity of a tree is not sufficient to characterize a neural network. The nodes of the same level of a tree cannot be connected with each other, i.e., these neural unit cannot share information with each other, which is a major drawback of ANN. Although ANN has been significantly improved in recent years to more complex structures, such as the directed acyclic graph (DAG), these methods also have unidirectional and acyclic bias for ANN. In this paper, we propose a method to build a bidirectional complete graph for the nodes in the same level of an ANN, which yokes the nodes of the same level to formulate a neural module. We call our model as YNN in short. YNN promotes the information transfer significantly which obviously helps in improving the performance of the method. Our YNN can imitate neural networks much better compared with the traditional ANN. In this paper, we analyze the existing structural bias of ANN and propose a model YNN to efficiently eliminate such structural bias. In our model, nodes also carry out aggregation and transformation of features, and edges determine the flow of information. We further impose auxiliary sparsity constraint to the distribution of connectedness, which promotes the learned structure to focus on critical connections. Finally, based on the optimized structure, we also design small neural module structure based on the minimum cut technique to reduce the computational burden of the YNN model. This learning process is compatible with the existing networks and different tasks. The obtained quantitative experimental results reflect that the learned connectivity is superior to the traditional NN structure.

Related papers

• Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning [78.88684753303794]
  Deep learning has predominantly advanced through applications in computer vision and natural language processing.<n>Neural operators are a principled way to generalize neural networks to mappings between function spaces.<n>This paper identifies and distills the key principles for constructing practical implementations of mappings between infinite-dimensional function spaces.
  arXiv  Detail & Related papers  (2025-06-12T17:59:31Z)
• Spatial embedding promotes a specific form of modularity with low entropy and heterogeneous spectral dynamics [0.0]
  Spatially embedded recurrent neural networks provide a promising avenue to study how modelled constraints shape the combined structural and functional organisation of networks over learning. We show that it is possible to study these restrictions through entropic measures of the neural weights and eigenspectrum, across both rate and spiking neural networks. This work deepens our understanding of constrained learning in neural networks, across coding schemes and tasks, where solutions to simultaneous structural and functional objectives must be accomplished in tandem.
  arXiv  Detail & Related papers  (2024-09-26T10:00:05Z)
• 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)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• Brain-inspired Evolutionary Architectures for Spiking Neural Networks [6.607406750195899]
  We explore efficient architectural optimization for Spiking Neural Networks (SNNs) This paper evolves SNNs architecture by incorporating brain-inspired local modular structure and global cross- module connectivity. We introduce an efficient multi-objective evolutionary algorithm based on a few-shot performance predictor, endowing SNNs with high performance, efficiency and low energy consumption.
  arXiv  Detail & Related papers  (2023-09-11T06:39:11Z)
• SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking Neural Networks [0.0]
  Spiking Neural Networks (SNNs) have attracted recent interest due to their energy efficiency and biological plausibility. There is no consensus on the best learning algorithm for SNNs. In this paper, we propose SPENSER, a framework for SNN generation based on DENSER.
  arXiv  Detail & Related papers  (2023-05-18T14:06:37Z)
• Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorization [12.1610509770913]
  Spiking Neural Networks (SNNs) mimic the spiking nature of brain neurons. We propose a joint training framework of ANN and SNN, in which the ANN can guide the SNN's optimization. Our method consistently outperforms many other state-of-the-art training methods.
  arXiv  Detail & Related papers  (2023-05-03T13:12:17Z)
• Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks [19.33517163587031]
  Spiking neural networks (SNNs) have superb characteristics in sensory information recognition tasks due to their biological plausibility. The performance of some current spiking-based models is limited by their structures which means either fully connected or too-deep structures bring too much redundancy. This paper proposes an evolutionary-based structure construction method for constructing more reasonable SNNs.
  arXiv  Detail & Related papers  (2023-04-19T08:41:17Z)
• 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)
• Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation [3.1247096708403914]
  Spiking neural networks (SNNs) have emerged as a low-power alternative to artificial neural networks (ANNs) In this work, we propose a hybrid SNN training scheme and apply it to segment human hippocampi from magnetic resonance images.
  arXiv  Detail & Related papers  (2023-02-14T20:18:57Z)
• Robust Knowledge Adaptation for Dynamic Graph Neural Networks [61.8505228728726]
  We propose Ada-DyGNN: a robust knowledge Adaptation framework via reinforcement learning for Dynamic Graph Neural Networks. Our approach constitutes the first attempt to explore robust knowledge adaptation via reinforcement learning. Experiments on three benchmark datasets demonstrate that Ada-DyGNN achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-07-22T02:06:53Z)
• Knowledge Enhanced Neural Networks for relational domains [83.9217787335878]
  We focus on a specific method, KENN, a Neural-Symbolic architecture that injects prior logical knowledge into a neural network. In this paper, we propose an extension of KENN for relational data.
  arXiv  Detail & Related papers  (2022-05-31T13:00:34Z)
• Functional2Structural: Cross-Modality Brain Networks Representation Learning [55.24969686433101]
  Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. We propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets.
  arXiv  Detail & Related papers  (2022-05-06T03:45:36Z)
• BScNets: Block Simplicial Complex Neural Networks [79.81654213581977]
  Simplicial neural networks (SNN) have recently emerged as the newest direction in graph learning. We present Block Simplicial Complex Neural Networks (BScNets) model for link prediction. BScNets outperforms state-of-the-art models by a significant margin while maintaining low costs.
  arXiv  Detail & Related papers  (2021-12-13T17:35:54Z)
• Spiking neural networks trained via proxy [0.696125353550498]
  We propose a new learning algorithm to train spiking neural networks (SNN) using conventional artificial neural networks (ANN) as proxy. We couple two SNN and ANN networks, respectively, made of integrate-and-fire (IF) and ReLU neurons with the same network architectures and shared synaptic weights. By assuming IF neuron with rate-coding as an approximation of ReLU, we backpropagate the error of the SNN in the proxy ANN to update the shared weights, simply by replacing the ANN final output with that of the SNN.
  arXiv  Detail & Related papers  (2021-09-27T17:29:51Z)
• Explore the Knowledge contained in Network Weights to Obtain Sparse Neural Networks [2.649890751459017]
  This paper proposes a novel learning approach to obtain sparse fully connected layers in neural networks (NNs) automatically. We design a switcher neural network (SNN) to optimize the structure of the task neural network (TNN)
  arXiv  Detail & Related papers  (2021-03-26T11:29:40Z)
• Kernel Based Progressive Distillation for Adder Neural Networks [71.731127378807]
  Adder Neural Networks (ANNs) which only contain additions bring us a new way of developing deep neural networks with low energy consumption. There is an accuracy drop when replacing all convolution filters by adder filters. We present a novel method for further improving the performance of ANNs without increasing the trainable parameters.
  arXiv  Detail & Related papers  (2020-09-28T03:29:19Z)
• A multi-agent model for growing spiking neural networks [0.0]
  This project has explored rules for growing the connections between the neurons in Spiking Neural Networks as a learning mechanism. Results in a simulation environment showed that for a given set of parameters it is possible to reach topologies that reproduce the tested functions. This project also opens the door to the usage of techniques like genetic algorithms for obtaining the best suited values for the model parameters.
  arXiv  Detail & Related papers  (2020-09-21T15:11:29Z)
• Neural Networks Enhancement with Logical Knowledge [83.9217787335878]
  We propose an extension of KENN for relational data. The results show that KENN is capable of increasing the performances of the underlying neural network even in the presence relational data.
  arXiv  Detail & Related papers  (2020-09-13T21:12:20Z)
• Locality Guided Neural Networks for Explainable Artificial Intelligence [12.435539489388708]
  We propose a novel algorithm for back propagation, called Locality Guided Neural Network(LGNN) LGNN preserves locality between neighbouring neurons within each layer of a deep network. In our experiments, we train various VGG and Wide ResNet (WRN) networks for image classification on CIFAR100.
  arXiv  Detail & Related papers  (2020-07-12T23:45:51Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)
• Automatic Organization of Neural Modules for Enhanced Collaboration in Neural Networks [0.0]
  This work proposes a new perspective on the structure of Neural Networks (NNs) Traditional NNs are typically tree-like structures for convenience. We introduce a synchronous graph-based structure to establish a novel way of organizing the neural units: the Neural Modules.
  arXiv  Detail & Related papers  (2020-05-08T15:05:37Z)

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.