Apr 17, 2020 · Our objective here is to understand \textit{what complicates Transformer training} from both empirical and theoretical perspectives. Our ...
Abstract. Transformers have proved effective in many NLP tasks. However, their training requires non-trivial efforts regarding carefully designing ...
Recent study shows that, even after introducing residual connections, the Transformer network still suffers from gradient vanishing.
We propose Adaptive Model Initialization (Admin), which successfully stabilizes previously-diverged Transformer training and achieves better performance.
Apr 20, 2020 · In this paper, we study Transformer training from both theoretical and empirical perspectives. Our analysis reveals that unbalanced gradients ...
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Do transformers take long to train?
This survey investigates popular approaches to make Transformers faster and lighter and provides a comprehensive explanation of the methods' strengths, ...
It explains why the standard SGD fails in training Transformers (i.e., lacking the ability to handle unbalanced gradients) and necessitates using adaptive ...
Apr 30, 2020 · In this paper, we study Transformer training from both theoretical and empirical perspectives. Our analysis reveals that unbalanced gradients ...
In this paper, we study Transformer training from both theoretical and empirical perspectives. Our analysis reveals that unbalanced gradients are not the root ...
A plug-in-and-play implementation of Admin, which stabilizes previously-diverged Transformer training and achieves better performance.