Reconstructing Deep Neural Networks: Unleashing the Optimization Potential of Natural Gradient Descent
- URL: http://arxiv.org/abs/2412.07441v1
- Date: Tue, 10 Dec 2024 11:57:47 GMT
- Title: Reconstructing Deep Neural Networks: Unleashing the Optimization Potential of Natural Gradient Descent
- Authors: Weihua Liu, Said Boumaraf, Jianwu Li, Chaochao Lin, Xiabi Liu, Lijuan Niu, Naoufel Werghi,
- Abstract summary: We propose a novel optimization method for training deep neural networks called structured natural gradient descent (SNGD)
Our proposed method has the potential to significantly improve the scalability and efficiency of NGD in deep learning applications.
- Score: 12.00557940490703
- License:
- Abstract: Natural gradient descent (NGD) is a powerful optimization technique for machine learning, but the computational complexity of the inverse Fisher information matrix limits its application in training deep neural networks. To overcome this challenge, we propose a novel optimization method for training deep neural networks called structured natural gradient descent (SNGD). Theoretically, we demonstrate that optimizing the original network using NGD is equivalent to using fast gradient descent (GD) to optimize the reconstructed network with a structural transformation of the parameter matrix. Thereby, we decompose the calculation of the global Fisher information matrix into the efficient computation of local Fisher matrices via constructing local Fisher layers in the reconstructed network to speed up the training. Experimental results on various deep networks and datasets demonstrate that SNGD achieves faster convergence speed than NGD while retaining comparable solutions. Furthermore, our method outperforms traditional GDs in terms of efficiency and effectiveness. Thus, our proposed method has the potential to significantly improve the scalability and efficiency of NGD in deep learning applications. Our source code is available at https://github.com/Chaochao-Lin/SNGD.
Related papers
- Improving Generalization of Deep Neural Networks by Optimum Shifting [33.092571599896814]
We propose a novel method called emphoptimum shifting, which changes the parameters of a neural network from a sharp minimum to a flatter one.
Our method is based on the observation that when the input and output of a neural network are fixed, the matrix multiplications within the network can be treated as systems of under-determined linear equations.
arXiv Detail & Related papers (2024-05-23T02:31:55Z) - Fixing the NTK: From Neural Network Linearizations to Exact Convex
Programs [63.768739279562105]
We show that for a particular choice of mask weights that do not depend on the learning targets, this kernel is equivalent to the NTK of the gated ReLU network on the training data.
A consequence of this lack of dependence on the targets is that the NTK cannot perform better than the optimal MKL kernel on the training set.
arXiv Detail & Related papers (2023-09-26T17:42:52Z) - 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) - Learning k-Level Structured Sparse Neural Networks Using Group Envelope Regularization [4.0554893636822]
We introduce a novel approach to deploy large-scale Deep Neural Networks on constrained resources.
The method speeds up inference time and aims to reduce memory demand and power consumption.
arXiv Detail & Related papers (2022-12-25T15:40:05Z) - A Novel Structured Natural Gradient Descent for Deep Learning [3.0686953242470794]
We reconstruct the structure of the deep neural network, and optimize the new network using traditional gradient descent (GD)
Experimental results show that our optimization method can accelerate the convergence of deep network models and achieve better performance than GD.
arXiv Detail & Related papers (2021-09-21T11:12:10Z) - Analytically Tractable Inference in Deep Neural Networks [0.0]
Tractable Approximate Inference (TAGI) algorithm was shown to be a viable and scalable alternative to backpropagation for shallow fully-connected neural networks.
We are demonstrating how TAGI matches or exceeds the performance of backpropagation, for training classic deep neural network architectures.
arXiv Detail & Related papers (2021-03-09T14:51:34Z) - Local Critic Training for Model-Parallel Learning of Deep Neural
Networks [94.69202357137452]
We propose a novel model-parallel learning method, called local critic training.
We show that the proposed approach successfully decouples the update process of the layer groups for both convolutional neural networks (CNNs) and recurrent neural networks (RNNs)
We also show that trained networks by the proposed method can be used for structural optimization.
arXiv Detail & Related papers (2021-02-03T09:30:45Z) - A Dynamical View on Optimization Algorithms of Overparameterized Neural
Networks [23.038631072178735]
We consider a broad class of optimization algorithms that are commonly used in practice.
As a consequence, we can leverage the convergence behavior of neural networks.
We believe our approach can also be extended to other optimization algorithms and network theory.
arXiv Detail & Related papers (2020-10-25T17:10:22Z) - 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) - Large Batch Training Does Not Need Warmup [111.07680619360528]
Training deep neural networks using a large batch size has shown promising results and benefits many real-world applications.
In this paper, we propose a novel Complete Layer-wise Adaptive Rate Scaling (CLARS) algorithm for large-batch training.
Based on our analysis, we bridge the gap and illustrate the theoretical insights for three popular large-batch training techniques.
arXiv Detail & Related papers (2020-02-04T23:03:12Z) - MSE-Optimal Neural Network Initialization via Layer Fusion [68.72356718879428]
Deep neural networks achieve state-of-the-art performance for a range of classification and inference tasks.
The use of gradient combined nonvolutionity renders learning susceptible to novel problems.
We propose fusing neighboring layers of deeper networks that are trained with random variables.
arXiv Detail & Related papers (2020-01-28T18:25:15Z)
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.