Summary of Attentionx: Exploiting Consensus Discrepancy in Attention From a Distributed Optimization Perspective, by Guoqiang Zhang and Richard Heusdens
AttentionX: Exploiting Consensus Discrepancy In Attention from A Distributed Optimization Perspective
by Guoqiang Zhang, Richard Heusdens
First submitted to arxiv on: 6 Sep 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 extends the standard Attention in transformers by introducing AttentionX, which exploits consensus discrepancy from a distributed optimization perspective. This approach is inspired by the primal-dual method of multipliers (PDMM), designed to solve distributed optimization problems over peer-to-peer networks. In PDMM, each node performs information-gathering and local information-fusion at each iteration, exploiting Lagrangian multipliers to capture historical consensus discrepancies. The authors propose AttentionX, incorporating this consensus discrepancy into the output update-expression of standard Attention. Experiments on ViT and nanoGPT show promising performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making a new kind of attention mechanism for transformers that works better by taking into account how different nodes in a network agree or disagree with each other. This idea comes from a way to solve optimization problems over networks, where nodes talk to their neighbors and update what they know based on what the others know. The new approach is called AttentionX and it combines this idea with the original attention mechanism. It works pretty well when tested on two different models. |
Keywords
» Artificial intelligence » Attention » Optimization » Vit
