🏛️ The Transformer architecture has revolutionized Natural Language Processing, being capable to beat the state-of-the-art on overwhelmingly numerous tasks! Check out this video for a brief history of the Transformer development.
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Paper links in order of appearance:
* 00:29 ImageNet challenge SOTA — https://paperswithcode.com/sota/image-classification-on-imagenet
* 00:58 Word2Vec — Mikolov, Tomas, et al. “Distributed representations of words and phrases and their compositionality.” Advances in neural information processing systems. 2013. https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
* 03:29 The Transformer — Vaswani, Ashish, et al. “Attention is all you need.” Advances in neural information processing systems. 2017. https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
* 04:30 Translating Programming Languages — Lachaux, Marie-Anne, et al. “Unsupervised Translation of Programming Languages”. 2020 https://arxiv.org/pdf/2006.03511.pdf
* 04:32 Symbolic Mathematics — Lample, Guillaume, and François Charton. “Deep learning for symbolic mathematics.” arXiv preprint arXiv:1912.01412 (2019). https://arxiv.org/pdf/1912.01412.pdf
* 04:37 Transformer Demo from Huggingface — https://transformer.huggingface.co/
* 04:52 BERT — Devlin, Jacob, et al. “BERT: Pre-training of deep bidirectional transformers for language understanding.” arXiv preprint arXiv:1810.04805 (2018). https://arxiv.org/pdf/1810.04805.pdf
* 06:29 Image Transformer — Parmar, Niki, et al. “Image transformer.” arXiv preprint arXiv:1802.05751 (2018). https://arxiv.org/pdf/1802.05751.pdf
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