Training neural networks without backpropagation using particles

• URL: http://arxiv.org/abs/2412.05667v2
• Date: Wed, 18 Dec 2024 12:57:22 GMT
• Title: Training neural networks without backpropagation using particles
• Authors: Deepak Kumar,
• Abstract summary: Neural networks are a group of neurons stacked together in multiple layers to mimic the biological neurons in a human brain. gradient descent strategy has been used to improve the backpropagation algorithm for neural networks. Particle swarm optimization (PSO) was proposed to select the best local minima among the search space of the loss function. In the proposed approach, we overcome the problem of gradient descent and the limitation of the PSO algorithm by training individual neurons separately.
• Score: 2.0750726261075014
• License:
• Abstract: Neural networks are a group of neurons stacked together in multiple layers to mimic the biological neurons in a human brain. Neural networks have been trained using the backpropagation algorithm based on gradient descent strategy for several decades. Several variants have been developed to improve the backpropagation algorithm. The loss function for the neural network is optimized through backpropagation, but several local minima exist in the manifold of the constructed neural network. We obtain several solutions matching the minima. The gradient descent strategy cannot avoid the problem of local minima and gets stuck in the minima due to the initialization. Particle swarm optimization (PSO) was proposed to select the best local minima among the search space of the loss function. The search space is limited to the instantiated particles in the PSO algorithm, and sometimes it cannot select the best solution. In the proposed approach, we overcome the problem of gradient descent and the limitation of the PSO algorithm by training individual neurons separately, capable of collectively solving the problem as a group of neurons forming a network. Our code and data are available at https://github.com/dipkmr/train-nn-wobp/

Related papers

• RelChaNet: Neural Network Feature Selection using Relative Change Scores [0.0]
  We introduce RelChaNet, a novel and lightweight feature selection algorithm that uses neuron pruning and regrowth in the input layer of a dense neural network. Our approach generally outperforms the current state-of-the-art methods, and in particular improves the average accuracy by 2% on the MNIST dataset.
  arXiv  Detail & Related papers  (2024-10-03T09:56:39Z)
• Minimum number of neurons in fully connected layers of a given neural network (the first approximation) [0.0]
  The paper presents an algorithm for searching for the minimum number of neurons in fully connected layers of an arbitrary network solving given problem. The proposed algorithm is the first approximation for estimating the minimum number of neurons in the layer, since, on the one hand, the algorithm does not guarantee that a neural network with the found number of neurons can be trained to the required quality.
  arXiv  Detail & Related papers  (2024-05-23T03:46:07Z)
• Benign Overfitting for Two-layer ReLU Convolutional Neural Networks [60.19739010031304]
  We establish algorithm-dependent risk bounds for learning two-layer ReLU convolutional neural networks with label-flipping noise. We show that, under mild conditions, the neural network trained by gradient descent can achieve near-zero training loss and Bayes optimal test risk.
  arXiv  Detail & Related papers  (2023-03-07T18:59:38Z)
• Globally Optimal Training of Neural Networks with Threshold Activation Functions [63.03759813952481]
  We study weight decay regularized training problems of deep neural networks with threshold activations. We derive a simplified convex optimization formulation when the dataset can be shattered at a certain layer of the network.
  arXiv  Detail & Related papers  (2023-03-06T18:59:13Z)
• Zonotope Domains for Lagrangian Neural Network Verification [102.13346781220383]
  We decompose the problem of verifying a deep neural network into the verification of many 2-layer neural networks. Our technique yields bounds that improve upon both linear programming and Lagrangian-based verification techniques.
  arXiv  Detail & Related papers  (2022-10-14T19:31:39Z)
• Adaptive Self-supervision Algorithms for Physics-informed Neural Networks [59.822151945132525]
  Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function. We study the impact of the location of the collocation points on the trainability of these models. We propose a novel adaptive collocation scheme which progressively allocates more collocation points to areas where the model is making higher errors.
  arXiv  Detail & Related papers  (2022-07-08T18:17:06Z)
• Neural Network Pruning Through Constrained Reinforcement Learning [3.2880869992413246]
  We propose a general methodology for pruning neural networks. Our proposed methodology can prune neural networks to respect pre-defined computational budgets. We prove the effectiveness of our approach via comparison with state-of-the-art methods on standard image classification datasets.
  arXiv  Detail & Related papers  (2021-10-16T11:57:38Z)
• Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks [19.216784367141972]
  We study the problem of estimating an unknown function from noisy data using shallow (single-hidden layer) ReLU neural networks. We quantify the performance of these neural network estimators when the data-generating function belongs to the space of functions of second-order bounded variation in the Radon domain.
  arXiv  Detail & Related papers  (2021-09-18T05:56:06Z)
• Topological obstructions in neural networks learning [67.8848058842671]
  We study global properties of the loss gradient function flow. We use topological data analysis of the loss function and its Morse complex to relate local behavior along gradient trajectories with global properties of the loss surface.
  arXiv  Detail & Related papers  (2020-12-31T18:53:25Z)
• Effective Version Space Reduction for Convolutional Neural Networks [61.84773892603885]
  In active learning, sampling bias could pose a serious inconsistency problem and hinder the algorithm from finding the optimal hypothesis. We examine active learning with convolutional neural networks through the principled lens of version space reduction.
  arXiv  Detail & Related papers  (2020-06-22T17:40:03Z)
• Improving the Backpropagation Algorithm with Consequentialism Weight Updates over Mini-Batches [0.40611352512781856]
  We show that it is possible to consider a multi-layer neural network as a stack of adaptive filters. We introduce a better algorithm by predicting then emending the adverse consequences of the actions that take place in BP even before they happen. Our experiments show the usefulness of our algorithm in the training of deep neural networks.
  arXiv  Detail & Related papers  (2020-03-11T08:45:36Z)

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.