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Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Updated: Jul 31, 2025

Gradient Echo Quantum Memory in Warm Atomic Vapor
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Long short-term memory with activation on gradient.

Chuan Qin1, Liangming Chen2, Zangtai Cai3

  • 1School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Xining 810008, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 7, 2023
PubMed
Summary
This summary is machine-generated.

Gradient activation improves Long Short-Term Memory (LSTM) networks by mitigating vanishing/exploding gradients and enhancing convergence. This method addresses training challenges in deep learning models.

Keywords:
Exploding gradient problemGradient activationIll-conditioned problemLong short-term memory (LSTM)Vanishing gradient problem

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Deep learning models, particularly Long Short-Term Memory (LSTM) networks, face performance degradation due to vanishing/exploding gradient problems as layer depth increases.
  • The ill-conditioned problem during LSTM training further impedes model convergence and stability.

Purpose of the Study:

  • To introduce and validate a gradient activation method for enhancing LSTM performance.
  • To establish empirical criteria for selecting optimal gradient activation hyperparameters.
  • To demonstrate the effectiveness of gradient activation in addressing common LSTM training issues.

Main Methods:

  • Application of a novel gradient activation technique to LSTM architectures.
  • Systematic comparison of various gradient activation functions and operations.
  • Empirical investigation to determine optimal hyperparameters for gradient activation.

Main Results:

  • Gradient activation effectively alleviates vanishing/exploding gradient problems in LSTMs.
  • The proposed method accelerates the convergence of LSTM training.
  • Empirical criteria for hyperparameter selection were successfully identified.

Conclusions:

  • Gradient activation is a simple yet effective strategy for improving LSTM performance and training dynamics.
  • This technique offers a practical solution for overcoming gradient-related challenges in deep recurrent neural networks.
  • The findings contribute to more stable and efficient training of deep learning models.