Summary of Conditional Computation in Neural Networks: Principles and Research Trends, by Simone Scardapane et al.
Conditional computation in neural networks: principles and research trends
by Simone Scardapane, Alessandro Baiocchi, Alessio Devoto, Valerio Marsocci, Pasquale Minervini, Jary Pomponi
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 conditional computation methods for neural networks dynamically activate or de-activate parts of their computational graph based on input. This includes selecting tokens, layers, and sub-modules. A formalism is introduced to describe these techniques uniformly, followed by implementations such as mixture-of-experts (MoEs) networks, token selection mechanisms, and early-exit neural networks. The benefits of modular designs are analyzed in terms of efficiency, explainability, and transfer learning, with applications in automated scientific discovery and semantic communication. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to make neural networks more flexible by letting them decide what parts to use based on the input. It’s like having a toolbox that can choose which tools to use depending on the job. The authors introduce a new way of thinking about this, called conditional computation, and show how it works in three different examples. This could be useful for things like helping scientists discover new things or sending information more efficiently. |
Keywords
* Artificial intelligence * Mixture of experts * Token * Transfer learning
