Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens

• URL: http://arxiv.org/abs/2310.14963v3
• Date: Thu, 13 Jun 2024 21:04:35 GMT
• Title: Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens
• Authors: Ross M. Clarke, José Miguel Hernández-Lobato,
• Abstract summary: We study AdamQLR: an optimiser combining damping and learning rate selection techniques from K-FAC. We evaluate AdamQLR on a range of regression and classification tasks at various scales. Finding an untuned AdamQLR setting can achieve comparable performance vs runtime to tuned benchmarks.
• Score: 34.72514951778262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Research into optimisation for deep learning is characterised by a tension between the computational efficiency of first-order, gradient-based methods (such as SGD and Adam) and the theoretical efficiency of second-order, curvature-based methods (such as quasi-Newton methods and K-FAC). Noting that second-order methods often only function effectively with the addition of stabilising heuristics (such as Levenberg-Marquardt damping), we ask how much these (as opposed to the second-order curvature model) contribute to second-order algorithms' performance. We thus study AdamQLR: an optimiser combining damping and learning rate selection techniques from K-FAC (Martens & Grosse, 2015) with the update directions proposed by Adam, inspired by considering Adam through a second-order lens. We evaluate AdamQLR on a range of regression and classification tasks at various scales and hyperparameter tuning methodologies, concluding K-FAC's adaptive heuristics are of variable standalone general effectiveness, and finding an untuned AdamQLR setting can achieve comparable performance vs runtime to tuned benchmarks.

Related papers

• Deconstructing What Makes a Good Optimizer for Language Models [7.9224468703944115]
  We compare several optimization algorithms, including SGD, Adafactor, Adam, and Lion, in the context of autoregressive language modeling. Our findings indicate that, except for SGD, these algorithms all perform comparably both in their optimal performance.
  arXiv  Detail & Related papers  (2024-07-10T18:11:40Z)
• Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees [63.18324983384337]
  We introduce the first learning to rank method for Gradient Boosted Decision Trees (GBDTs) Our main contribution is a novel estimator for the second-order derivatives, i.e., the Hessian matrix. We incorporate our estimator into the existing PL-Rank framework, which was originally designed for first-order derivatives only.
  arXiv  Detail & Related papers  (2024-04-18T13:53:32Z)
• Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers [109.52244418498974]
  We propose a novel textscAdmeta (textbfADouble exponential textbfMov averagtextbfE textbfAdaptive and non-adaptive momentum) framework. We provide two implementations, textscAdmetaR and textscAdmetaS, the former based on RAdam and the latter based on SGDM.
  arXiv  Detail & Related papers  (2023-07-02T18:16:06Z)
• Improved Algorithms for Neural Active Learning [74.89097665112621]
  We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. We introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work.
  arXiv  Detail & Related papers  (2022-10-02T05:03:38Z)
• A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning [0.6526824510982802]
  Adaptive gradient methods have become popular in optimizing deep neural networks. Recent examples include AdaGrad and Adam. We develop a generic framework for adaptive gradient methods.
  arXiv  Detail & Related papers  (2022-06-04T17:55:33Z)
• On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning [0.0]
  We study the behaviour of quasi-Newton training algorithms for deep memory networks. We show that quasi-Newtons are efficient and able to outperform in some instances the well-known first-order Adam run.
  arXiv  Detail & Related papers  (2022-05-18T20:53:58Z)
• Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization [118.50301177912381]
  We show that Adam can converge to different solutions of the objective with provably different errors, even with weight decay globalization. We show that if convex, and the weight decay regularization is employed, any optimization algorithms including Adam will converge to the same solution.
  arXiv  Detail & Related papers  (2021-08-25T17:58:21Z)
• Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent [20.47598828422897]
  We propose textit-Meta-Regularization, a novel approach for the adaptive choice of the learning rate in first-order descent methods. Our approach modifies the objective function by adding a regularization term, and casts the joint process parameters.
  arXiv  Detail & Related papers  (2021-04-12T13:13:34Z)
• Meta-Learning with Neural Tangent Kernels [58.06951624702086]
  We propose the first meta-learning paradigm in the Reproducing Kernel Hilbert Space (RKHS) induced by the meta-model's Neural Tangent Kernel (NTK) Within this paradigm, we introduce two meta-learning algorithms, which no longer need a sub-optimal iterative inner-loop adaptation as in the MAML framework. We achieve this goal by 1) replacing the adaptation with a fast-adaptive regularizer in the RKHS; and 2) solving the adaptation analytically based on the NTK theory.
  arXiv  Detail & Related papers  (2021-02-07T20:53:23Z)
• ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning [91.13797346047984]
  We introduce ADAHESSIAN, a second order optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates. We show that ADAHESSIAN achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods.
  arXiv  Detail & Related papers  (2020-06-01T05:00:51Z)

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.