Summary of Badm: Batch Admm For Deep Learning, by Ouya Wang et al.
BADM: Batch ADMM for Deep Learning
by Ouya Wang, Shenglong Zhou, Geoffrey Ye Li
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed batch ADMM (BADM) algorithm leverages the alternating direction method of multipliers framework to develop a novel data-driven approach for training deep neural networks. By splitting the training data into batches, which are further divided into sub-batches, BADM updates primal and dual variables to generate global parameters through aggregation. The algorithm is evaluated across various deep learning tasks, including graph modelling, computer vision, image generation, and natural language processing. Results show that BADM achieves faster convergence and superior testing accuracy compared to other state-of-the-art optimizers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to train deep neural networks using an algorithm called batch ADMM (BADM). Right now, these networks can take a long time to learn. The authors found a way to split the data into smaller groups and then combine it to make learning faster and more accurate. They tested this method on different tasks like recognizing images, understanding natural language, and modeling graphs. The results show that BADM is better than other methods at both learning quickly and getting correct answers. |
Keywords
» Artificial intelligence » Deep learning » Image generation » Natural language processing
