Adding vs. concatenating positional embeddings & Learned positional encodings

Adding vs. concatenating positional embeddings & Learned positional encodings

Jul 25, 2021
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When to add and when to concatenate positional embeddings? What are arguments for learning positional encodings? When to hand-craft them? Ms. Coffee Bean’s answers these questions in this video. ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 🔥 Optionally, help us boost our Coffee Bean production! ☕ Patreon: https://www.patreon.com/AICoffeeBreak Ko-fi: https://ko-fi.com/aicoffeebreak ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 📺 Positional embeddings explained: https://youtu.be/1biZfFLPRSY 📺 Fourier Transform instead of attention: https://youtu.be/j7pWPdGEfMA 📺 Transformer explained: https://youtu.be/FWFA4DGuzSc Outline: 00:00 Concatenated vs. added positional embeddings 04:49 Learned positional embeddings 06:48 Ms. Coffee Bean deepest insight ever Papers 📄: Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. "Attention is all you need." In Advances in neural information processing systems, pp. 5998-6008. 2017. https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf Wang, Yu-An, and Yun-Nung Chen. "What do position embeddings learn? an empirical study of pre-trained language model positional encoding." arXiv preprint arXiv:2010.04903 (2020). https://arxiv.org/pdf/2010.04903.pdf Dosovitskiy, Alexey, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani et al. "An image is worth 16x16 words: Transformers for image recognition at scale." arXiv preprint arXiv:2010.11929 (2020). https://arxiv.org/abs/2010.11929 🔗 Links: AICoffeeBreakQuiz: https://www.youtube.com/c/AICoffeeBreak/community Twitter: https://twitter.com/AICoffeeBreak Reddit: https://www.reddit.com/r/AICoffeeBreak/ YouTube: https://www.youtube.com/AICoffeeBreak #AICoffeeBreak #MsCoffeeBean #MachineLearning #AI #research​

00:00 Concatenated vs. added positional embeddings 04:49 Learned positional embeddings 06:48 Ms. Coffee Bean deepest insight ever
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