Summary of Learning Randomized Algorithms with Transformers, by Johannes Von Oswald and Seijin Kobayashi and Yassir Akram and Angelika Steger
Learning Randomized Algorithms with Transformers
by Johannes von Oswald, Seijin Kobayashi, Yassir Akram, Angelika Steger
First submitted to arxiv on: 20 Aug 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 This paper enhances deep transformer models by incorporating randomness into their training process. Randomized algorithms are known for excelling in adversarial settings, often outperforming deterministic algorithms with significant margins. By instilling randomization into transformers through learning, the authors demonstrate that this approach can be effective in various tasks, including associative recall, graph coloring, and agent exploration in grid worlds. The results show increased robustness against oblivious adversaries and remarkable performance improvements due to the inherently random nature of neural networks’ computation and predictions. The authors analyze known adversarial objectives where randomized algorithms offer a distinct advantage over deterministic ones and explore common optimization techniques like gradient descent or evolutionary strategies to learn transformer parameters that utilize randomness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes deep learning models stronger by adding randomness to their training process. Randomness helps algorithms perform well even when they’re faced with unexpected challenges. The authors show how this approach can be used in different tasks, such as remembering associations, coloring graphs, and helping agents explore a world. The results demonstrate that these randomized neural networks are more robust against unexpected problems and can make better predictions. This is an important finding because it shows how randomization can improve the performance of deep learning models. |
Keywords
» Artificial intelligence » Deep learning » Gradient descent » Optimization » Recall » Transformer
