Summary of Local to Global: Learning Dynamics and Effect Of Initialization For Transformers, by Ashok Vardhan Makkuva et al.
Local to Global: Learning Dynamics and Effect of Initialization for Transformers
by Ashok Vardhan Makkuva, Marco Bondaschi, Chanakya Ekbote, Adway Girish, Alliot Nagle, Hyeji Kim, Michael Gastpar
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Machine Learning (stat.ML)
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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 Transformers have revolutionized sequence modeling, and researchers are keen to understand how they learn Markov chains. Despite the importance of this topic, many fundamental questions remain unanswered. This paper addresses these gaps by studying first-order Markov chains and single-layer transformers. The authors prove that transformer parameters can converge to global or local minima depending on initialization and data properties, and characterize the conditions for each scenario. Empirical evidence confirms these findings, leading to guidelines for initializing transformer parameters. Code is available at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are super smart computers that can understand sequences of information. To get better at understanding sequences, scientists want to know how transformers learn about patterns in data. Right now, we don’t fully understand this process, so researchers are trying to figure it out. This paper helps by studying a special kind of pattern called Markov chains and a type of transformer that only looks one step ahead. The authors discover that the way they start the learning process affects how well they do in the end. They also show that their ideas work in real-world experiments. |
Keywords
» Artificial intelligence » Transformer
