Towards Practical Second-Order Optimizers in Deep Learning: Insights from Fisher Information Analysis

• URL: http://arxiv.org/abs/2504.20096v1
• Date: Sat, 26 Apr 2025 05:02:21 GMT
• Title: Towards Practical Second-Order Optimizers in Deep Learning: Insights from Fisher Information Analysis
• Authors: Damien Martins Gomes,
• Abstract summary: We present AdaFisher, a novel adaptive second-order tuning for deep neural networks (DNNs)<n>AdaFisher aims to bridge the gap between the improved convergence and generalization of second-order methods and the computational efficiency needed for trainings.<n>We demonstrate that AdaFisher outperforms state-of-the-art approximations in both accuracy and convergence speed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: First-order optimization methods remain the standard for training deep neural networks (DNNs). Optimizers like Adam incorporate limited curvature information by preconditioning the stochastic gradient with a diagonal matrix. Despite the widespread adoption of first-order methods, second-order optimization algorithms often exhibit superior convergence compared to methods like Adam and SGD. However, their practicality in training DNNs is still limited by a significantly higher per-iteration computational cost compared to first-order methods. In this thesis, we present AdaFisher, a novel adaptive second-order optimizer that leverages a diagonal block-Kronecker approximation of the Fisher information matrix to adaptively precondition gradients. AdaFisher aims to bridge the gap between the improved convergence and generalization of second-order methods and the computational efficiency needed for training DNNs. Despite the traditionally slower speed of second-order optimizers, AdaFisher is effective for tasks such as image classification and language modeling, exhibiting remarkable stability and robustness during hyperparameter tuning. We demonstrate that AdaFisher outperforms state-of-the-art optimizers in both accuracy and convergence speed. The code is available from https://github.com/AtlasAnalyticsLab/AdaFisher.

Related papers

• MARS: Unleashing the Power of Variance Reduction for Training Large Models [56.47014540413659]
  We propose a unified training framework for deep neural networks.<n>We introduce three instances of MARS that leverage preconditioned gradient optimization.<n>Results indicate that the implementation of MARS consistently outperforms Adam.
  arXiv  Detail & Related papers  (2024-11-15T18:57:39Z)
• Understanding Optimization in Deep Learning with Central Flows [53.66160508990508]
  We show that an RMS's implicit behavior can be explicitly captured by a "central flow:" a differential equation. We show that these flows can empirically predict long-term optimization trajectories of generic neural networks.
  arXiv  Detail & Related papers  (2024-10-31T17:58:13Z)
• Efficient Second-Order Neural Network Optimization via Adaptive Trust Region Methods [0.0]
  SecondOrderAdaptive (SOAA) is a novel optimization algorithm designed to overcome limitations of traditional second-order techniques. We empirically demonstrate that SOAA achieves faster and more stable convergence compared to first-order approximations.
  arXiv  Detail & Related papers  (2024-10-03T08:23:06Z)
• AdaFisher: Adaptive Second Order Optimization via Fisher Information [22.851200800265914]
  First-order optimization methods are currently the mainstream in training deep neural networks (DNNs).s like Adam incorporate limited curvature information by employing the matrix preconditioning of the gradient during the training.<n>Despite their widespread, second-order optimization algorithms exhibit superior convergence properties compared to their first-order counterparts e.g. Adam and SGD.<n>We present emphAdaFisher--an adaptive second-order that leverages a emphdiagonal block-Kronecker approximation of the Fisher information matrix for adaptive gradient preconditioning.
  arXiv  Detail & Related papers  (2024-05-26T01:25:02Z)
• SGD with Partial Hessian for Deep Neural Networks Optimization [18.78728272603732]
  We propose a compound, which is a combination of a second-order with a precise partial Hessian matrix for updating channel-wise parameters and the first-order gradient descent (SGD) algorithms for updating the other parameters. Compared with first-orders, it adopts a certain amount of information from the Hessian matrix to assist optimization, while compared with the existing second-order generalizations, it keeps the good performance of first-order generalizations imprecise.
  arXiv  Detail & Related papers  (2024-03-05T06:10:21Z)
• ELRA: Exponential learning rate adaption gradient descent optimization method [83.88591755871734]
  We present a novel, fast (exponential rate), ab initio (hyper-free) gradient based adaption. The main idea of the method is to adapt the $alpha by situational awareness. It can be applied to problems of any dimensions n and scales only linearly.
  arXiv  Detail & Related papers  (2023-09-12T14:36:13Z)
• Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models [134.83964935755964]
  In deep learning, different kinds of deep networks typically need different extrapolations, which have to be chosen after multiple trials.<n>To relieve this issue and consistently improve the model training speed deep networks, we propose the ADAtive Nesterov momentum Transformer.
  arXiv  Detail & Related papers  (2022-08-13T16:04:39Z)
• Joint inference and input optimization in equilibrium networks [68.63726855991052]
  deep equilibrium model is a class of models that foregoes traditional network depth and instead computes the output of a network by finding the fixed point of a single nonlinear layer. We show that there is a natural synergy between these two settings. We demonstrate this strategy on various tasks such as training generative models while optimizing over latent codes, training models for inverse problems like denoising and inpainting, adversarial training and gradient based meta-learning.
  arXiv  Detail & Related papers  (2021-11-25T19:59:33Z)
• Second-order Neural Network Training Using Complex-step Directional Derivative [41.4333906662624]
  We introduce a numerical algorithm for second-order neural network training. We tackle the practical obstacle of Hessian calculation by using the complex-step finite difference. We believe our method will inspire a wide-range of new algorithms for deep learning and numerical optimization.
  arXiv  Detail & Related papers  (2020-09-15T13:46:57Z)
• Scalable Second Order Optimization for Deep Learning [34.12384996822749]
  We present a scalable implementation of a second-order preconditioned method (concretely, a variant of full-matrix Adagrad) Our novel design effectively utilizes the prevalent heterogeneous hardware architecture for training deep models, consisting of a multicore CPU coupled with multiple accelerator units. We demonstrate superior performance compared to state-of-the-art on very large learning tasks such as machine translation with Transformers, language modeling with BERT, click-through rate prediction on Criteo, and image classification on ImageNet with ResNet-50.
  arXiv  Detail & Related papers  (2020-02-20T20:51:33Z)
• Self-Directed Online Machine Learning for Topology Optimization [58.920693413667216]
  Self-directed Online Learning Optimization integrates Deep Neural Network (DNN) with Finite Element Method (FEM) calculations. Our algorithm was tested by four types of problems including compliance minimization, fluid-structure optimization, heat transfer enhancement and truss optimization. It reduced the computational time by 2 5 orders of magnitude compared with directly using methods, and outperformed all state-of-the-art algorithms tested in our experiments.
  arXiv  Detail & Related papers  (2020-02-04T20:00:28Z)

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.