# Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning

arXiv: https://arxiv.org/abs/2003.00231 · HTML: https://mlsanity.com/paper/2003.00231/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [Adaptive Learning Rate and Momentum for Training Deep Neural Networks](https://mlsanity.com/paper/2106.11548/) (2021)
- [Conjugate-Gradient-like Based Adaptive Moment Estimation Optimization Algorithm for Deep Learning](https://mlsanity.com/paper/2404.01714/) (2024)

## Abstract (arXiv)

This paper proposes a conjugate-gradient-based Adam algorithm blending Adam with nonlinear conjugate gradient methods and shows its convergence analysis. Numerical experiments on text classification and image classification show that the proposed algorithm can train deep neural network models in fewer epochs than the existing adaptive stochastic optimization algorithms can.