EP31: Attention is not all you need - Pure attention loses rank doubly exponentially with depth by Yihe Dong, Jean-Baptiste Cordonnier and Andreas Loukas

Disclaimer: This podcast is completely AI generated by ⁠⁠⁠⁠⁠NoteBookLM⁠⁠⁠⁠⁠ 🤖 Summary During this episode we discuss this paper that investigates the effectiveness of self-attention networks (SANs) in deep learning models. The authors prove that pure SANs, without skip connections or multi-layer perceptrons (MLPs), experience a rapid loss of expressiveness, converging doubly exponentially to a rank-1 matrix as the network depth increases. This means that all tokens become identical, losing their individuality and reducing the model's ability to capture complex relationships in data. However, the authors find that skip connections effectively counteract this rank collapse, while MLPs can slow down the convergence. They propose a novel path decomposition method to analyse the behaviour of SANs, revealing that they effectively function as ensembles of shallow networks. This research highlights the critical role of skip connections and MLPs in mitigating the limitations of pure self-attention, providing valuable insights for building more robust and effective deep learning models.

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