CoRe Optimizer: An All-in-One Solution for Machine Learning

• URL: http://arxiv.org/abs/2307.15663v2
• Date: Sun, 18 Feb 2024 00:33:34 GMT
• Title: CoRe Optimizer: An All-in-One Solution for Machine Learning
• Authors: Marco Eckhoff and Markus Reiher
• Abstract summary: Continuously resilient convergence (CoRe) shown superior performance compared to other state-of-the-art first-order gradient-based convergence algorithms. CoRe yields best or competitive performance in every investigated application.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The optimization algorithm and its hyperparameters can significantly affect the training speed and resulting model accuracy in machine learning applications. The wish list for an ideal optimizer includes fast and smooth convergence to low error, low computational demand, and general applicability. Our recently introduced continual resilient (CoRe) optimizer has shown superior performance compared to other state-of-the-art first-order gradient-based optimizers for training lifelong machine learning potentials. In this work we provide an extensive performance comparison of the CoRe optimizer and nine other optimization algorithms including the Adam optimizer and resilient backpropagation (RPROP) for diverse machine learning tasks. We analyze the influence of different hyperparameters and provide generally applicable values. The CoRe optimizer yields best or competitive performance in every investigated application, while only one hyperparameter needs to be changed depending on mini-batch or batch learning.

Related papers

• Combining Automated Optimisation of Hyperparameters and Reward Shape [7.407166175374958]
  We propose a methodology for the combined optimisation of hyperparameters and the reward function. We conducted extensive experiments using Proximal Policy optimisation and Soft Actor-Critic. Our results show that combined optimisation significantly improves over baseline performance in half of the environments and achieves competitive performance in the others.
  arXiv  Detail & Related papers  (2024-06-26T12:23:54Z)
• MADA: Meta-Adaptive Optimizers through hyper-gradient Descent [73.1383658672682]
  We introduce Meta-Adaptives (MADA), a unified framework that can generalize several known convergences and dynamically learn the most suitable one during training. We empirically compare MADA to other populars on vision and language tasks, and find that MADA consistently outperforms Adam and other populars. We also propose AVGrad, a modification of AMS that replaces the maximum operator with averaging, which is more suitable for hyper-gradient optimization.
  arXiv  Detail & Related papers  (2024-01-17T00:16:46Z)
• VeLO: Training Versatile Learned Optimizers by Scaling Up [67.90237498659397]
  We leverage the same scaling approach behind the success of deep learning to learn versatiles. We train an ingest for deep learning which is itself a small neural network that ingests and outputs parameter updates. We open source our learned, meta-training code, the associated train test data, and an extensive benchmark suite with baselines at velo-code.io.
  arXiv  Detail & Related papers  (2022-11-17T18:39:07Z)
• Learning to Optimize Quasi-Newton Methods [22.504971951262004]
  This paper introduces a novel machine learning called LODO, which tries to online meta-learn the best preconditioner during optimization. Unlike other L2O methods, LODO does not require any meta-training on a training task distribution. We show that our gradient approximates the inverse Hessian in noisy loss landscapes and is capable of representing a wide range of inverse Hessians.
  arXiv  Detail & Related papers  (2022-10-11T03:47:14Z)
• A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases [44.01339030872185]
  Blackbox learneds often struggle with stability and generalization when applied to tasks unlike those in their meta-training set. We investigate the inductive biases and stability properties of optimization algorithms, and apply the resulting insights to designing inductive biases for blackboxs. We learn to a variety of neural network training tasks, where it outperforms the current state of the art learned.
  arXiv  Detail & Related papers  (2022-09-22T17:47:21Z)
• Practical tradeoffs between memory, compute, and performance in learned optimizers [46.04132441790654]
  We identify and quantify the memory, compute, and performance trade-offs for many learned and hand-designeds features. We leverage our analysis to construct a learned is both faster and more efficient than previous work.
  arXiv  Detail & Related papers  (2022-03-22T16:36:36Z)
• Training Learned Optimizers with Randomly Initialized Learned Optimizers [49.67678615506608]
  We show that a population of randomly learneds can be used to train themselves from scratch in an online fashion. A form of population based training is used to orchestrate this self-training. We believe feedback loops of this type will be important and powerful in the future of machine learning.
  arXiv  Detail & Related papers  (2021-01-14T19:07:17Z)
• Reverse engineering learned optimizers reveals known and novel mechanisms [50.50540910474342]
  Learneds are algorithms that can themselves be trained to solve optimization problems. Our results help elucidate the previously murky understanding of how learneds work, and establish tools for interpreting future learneds.
  arXiv  Detail & Related papers  (2020-11-04T07:12:43Z)
• Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves [53.37905268850274]
  We introduce a new, hierarchical, neural network parameterized, hierarchical with access to additional features such as validation loss to enable automatic regularization. Most learneds have been trained on only a single task, or a small number of tasks. We train ours on thousands of tasks, making use of orders of magnitude more compute, resulting in generalizes that perform better to unseen tasks.
  arXiv  Detail & Related papers  (2020-09-23T16:35:09Z)

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.