Summary of Aa-dladmm: An Accelerated Admm-based Framework For Training Deep Neural Networks, by Zeinab Ebrahimi et al.
AA-DLADMM: An Accelerated ADMM-based Framework for Training Deep Neural Networks
by Zeinab Ebrahimi, Gustavo Batista, Mohammad Deghat
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an Anderson Acceleration for Deep Learning ADMM (AA-DLADMM) algorithm as an alternative to traditional stochastic gradient descent (SGD) methods. The authors address the limitations of SGD, such as vanishing gradients and lack of theoretical guarantees, by incorporating Alternating Direction Method of Multipliers (ADMM). While ADMM-based optimizers have a slow convergence rate, the AA-DLADMM algorithm leverages Anderson acceleration to achieve nearly quadratic convergence rates. Experimental results on four benchmark datasets demonstrate the effectiveness and efficiency of the proposed method, outperforming state-of-the-art optimizers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps train deep neural networks more efficiently. Right now, we use something called stochastic gradient descent (SGD), but it has some big problems like slow learning and not being very reliable. The authors suggest using a different approach called Alternating Direction Method of Multipliers (ADMM) to fix these issues. However, ADMM-based methods can be slow too. To solve this, the paper proposes an Anderson Acceleration for Deep Learning ADMM (AA-DLADMM) algorithm that makes ADMM faster and more reliable. The authors test their idea on four big datasets and show that it works better than other popular methods. |
Keywords
* Artificial intelligence * Deep learning * Stochastic gradient descent
