Summary of What Comes After Transformers? — a Selective Survey Connecting Ideas in Deep Learning, by Johannes Schneider
What comes after transformers? – A selective survey connecting ideas in deep learning
by Johannes Schneider
First submitted to arxiv on: 1 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 The paper provides an overview of recent developments in deep learning, specifically novel alternative approaches to transformers and successful ideas from the past decade. It highlights key strategies for innovation, including state-of-the-art models such as OpenAI’s GPT series, Meta’s LLama models, and Google’s Gemini model family. The authors discuss attempts to improve on transformers, covering both proven methods like state space models and promising but non-state-of-the-art ideas. They identify patterns that summarize the success of recent innovations in deep learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how artificial intelligence has improved recently. It talks about a type of model called transformers, which are very good at some things but have some problems too. The authors look at lots of different ways people have tried to make these models better, including some new ideas that might be useful. They also talk about the most advanced models right now, like GPT and LLama. The goal is to help researchers understand what’s going on in this field and how they can use it to make even more progress. |
Keywords
» Artificial intelligence » Deep learning » Gemini » Gpt » Llama
