WebYee Whye Teh's 296 research works with 15,787 citations and 11,866 reads, including: Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation Webcan train deeper Transformers without using layer normalisation. @L @x l = @L @x L (1 + LX 1 m=l z m @F m(x m) @x l) (6) 2.2 Multilingual Latent Layers It is sometimes convenient to share a Transformer network across multiple languages, enabling crosslingual transfer, with recent success in multilingual machine translation and multilingual pre-

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Webopenreview.net WebAll curves average over 3 seeds. from publication: Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation Skip connections and normalisation layers form ... host of one question

D TRANSFORMERS WITHOUT SHORTCUTS M S …

Webstudy the problem of signal propagation and rank collapse in deep skipless transformers, and derive three approaches to prevent it in Section3. Our methods use combinations of: 1) parameter ini- WebA Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others ... X-Pruner: eXplainable Pruning for Vision Transformers Lu Yu · Wei Xiang Deep Graph Reprogramming ... Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models ... Webtransformers. A transformer without shortcut suffer extremely low performance (Table 1). Empirically, removing the shortcut results in features from different patches becoming indistinguishable as the network going deeper (shown in Figure 3(a)), and such features have limited representation capacity for the downstream prediction. host of one step beyond