Summary of Pidformer: Transformer Meets Control Theory, by Tam Nguyen et al.
PIDformer: Transformer Meets Control Theory
by Tam Nguyen, César A. Uribe, Tan M. Nguyen, Richard G. Baraniuk
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Systems and Control (eess.SY)
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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 addresses two key limitations of transformer architectures: input corruption and rank collapse in their output representation. By viewing self-attention as an autonomous state-space model, the authors show that it inherently promotes smoothness in its solutions, leading to lower-rank outputs and reduced representation capacity. The researchers incorporate a Proportional-Integral-Derivative (PID) closed-loop feedback control system with a reference point into the model to improve robustness and representation capacity. This integration aims to preserve high-frequency details while bolstering model stability, rendering it more noise-resilient. The resulting controlled state-space model is theoretically proven robust and adept at addressing rank collapse. A novel class of transformers, called PIDformer, is derived from this control framework to improve robustness and mitigate the rank-collapse issue inherent in softmax transformers. Empirical evaluations demonstrate the advantages and robustness of PIDformer across various practical tasks, including object classification, image segmentation, and language modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to fix two problems with a type of artificial intelligence called transformers. Transformers can’t handle messy input data very well, and they also tend to lose important details. The researchers found that if we think of the part of the transformer that looks at all the different pieces of information as a kind of control system, it can actually help make the transformer more robust and better at handling messy data. They used this idea to create a new type of transformer that’s better at dealing with noisy data and preserving important details. This new transformer was tested on various tasks, such as recognizing objects in pictures, segmenting images into different parts, and understanding language. |
Keywords
» Artificial intelligence » Classification » Image segmentation » Self attention » Softmax » Transformer
