Tuning Learning Rates with the Cumulative-Learning Constant

• URL: http://arxiv.org/abs/2505.13457v1
• Date: Wed, 30 Apr 2025 00:07:48 GMT
• Title: Tuning Learning Rates with the Cumulative-Learning Constant
• Authors: Nathan Faraj,
• Abstract summary: A previously unrecognized proportionality between learning rates and dataset sizes is discovered.<n>A cumulative learning constant is identified, offering a framework for designing and optimizing advanced learning rate schedules.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a novel method for optimizing learning rates in machine learning. A previously unrecognized proportionality between learning rates and dataset sizes is discovered, providing valuable insights into how dataset scale influences training dynamics. Additionally, a cumulative learning constant is identified, offering a framework for designing and optimizing advanced learning rate schedules. These findings have the potential to enhance training efficiency and performance across a wide range of machine learning applications.

Related papers

• Efficient Machine Unlearning via Influence Approximation [75.31015485113993]
  Influence-based unlearning has emerged as a prominent approach to estimate the impact of individual training samples on model parameters without retraining.<n>This paper establishes a theoretical link between memorizing (incremental learning) and forgetting (unlearning)<n>We introduce the Influence Approximation Unlearning algorithm for efficient machine unlearning from the incremental perspective.
  arXiv  Detail & Related papers  (2025-07-31T05:34:27Z)
• Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics [14.157213827899342]
  Large language models (LLMs) are revolutionizing the field of education by enabling personalized learning experiences tailored to individual student needs.<n>This paper introduces a framework for Adaptive Learning Systems that leverages LLM-powered analytics for personalized curriculum design.
  arXiv  Detail & Related papers  (2025-07-25T04:36:17Z)
• Machine Learning-Driven Student Performance Prediction for Enhancing Tiered Instruction [11.564820268803619]
  Student performance prediction is one of the most important subjects in educational data mining.<n>Despite extensive prediction experiments, machine learning methods have not been effectively integrated into practical teaching strategies.<n>This study integrates the results of machine learning-based student performance prediction with tiered instruction, aiming to enhance student outcomes in target course.
  arXiv  Detail & Related papers  (2025-02-05T13:13:25Z)
• Capturing the Temporal Dependence of Training Data Influence [100.91355498124527]
  We formalize the concept of trajectory-specific leave-one-out influence, which quantifies the impact of removing a data point during training.<n>We propose data value embedding, a novel technique enabling efficient approximation of trajectory-specific LOO.<n>As data value embedding captures training data ordering, it offers valuable insights into model training dynamics.
  arXiv  Detail & Related papers  (2024-12-12T18:28:55Z)
• Accelerating Deep Learning with Fixed Time Budget [2.190627491782159]
  This paper proposes an effective technique for training arbitrary deep learning models within fixed time constraints. The proposed method is extensively evaluated in both classification and regression tasks in computer vision.
  arXiv  Detail & Related papers  (2024-10-03T21:18:04Z)
• Improving Knowledge Distillation in Transfer Learning with Layer-wise Learning Rates [6.783548275689542]
  We propose a layer-wise learning scheme that adjusts learning parameters per layer as a function of the differences in the Jacobian/Attention/Hessian of the output activations. We received improved learning performance and stability against a wide range of datasets.
  arXiv  Detail & Related papers  (2024-07-05T21:35:17Z)
• PILOT: A Pre-Trained Model-Based Continual Learning Toolbox [65.57123249246358]
  This paper introduces a pre-trained model-based continual learning toolbox known as PILOT.<n>On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt.<n>On the other hand, PILOT fits typical class-incremental learning algorithms within the context of pre-trained models to evaluate their effectiveness.
  arXiv  Detail & Related papers  (2023-09-13T17:55:11Z)
• Mechanic: A Learning Rate Tuner [52.4242550204696]
  We introduce a technique for tuning the learning rate scale factor of any base optimization algorithm and schedule automatically, which we call textscmechanic. We rigorously evaluate textscmechanic on a range of large scale deep learning tasks with varying batch sizes, schedules, and base optimization algorithms.
  arXiv  Detail & Related papers  (2023-05-31T19:32:43Z)
• Time Series Contrastive Learning with Information-Aware Augmentations [57.45139904366001]
  A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples. How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question. We propose a new contrastive learning approach with information-aware augmentations, InfoTS, that adaptively selects optimal augmentations for time series representation learning.
  arXiv  Detail & Related papers  (2023-03-21T15:02:50Z)
• Learning Rate Perturbation: A Generic Plugin of Learning Rate Schedule towards Flatter Local Minima [40.70374106466073]
  We propose a generic learning rate schedule plugin called LEArning Rate Perturbation (LEAP) LEAP can be applied to various learning rate schedules to improve the model training by introducing a certain perturbation to the learning rate. We conduct extensive experiments which show that training with LEAP can improve the performance of various deep learning models on diverse datasets.
  arXiv  Detail & Related papers  (2022-08-25T05:05:18Z)
• BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search engines [49.75878234192369]
  We present WEAVER, a simple, yet efficient post-processing method that infuses old knowledge into the new model. We show that applying WEAVER in a sequential manner results in similar word embedding distributions as doing a combined training on all data at once.
  arXiv  Detail & Related papers  (2022-02-21T10:34:41Z)
• Training With Data Dependent Dynamic Learning Rates [8.833548357664608]
  We propose an optimization framework which accounts for difference in loss function characteristics across instances. Our framework learns a dynamic learning rate for each instance present in the dataset. We show that our framework can be used for personalization of a machine learning model towards a known targeted data distribution.
  arXiv  Detail & Related papers  (2021-05-27T21:52:29Z)
• Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates [52.164757178369804]
  Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget. We conduct an empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep pre-trained models in the active learning framework. We also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance.
  arXiv  Detail & Related papers  (2021-01-20T13:59:25Z)

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.