第十八章 深度学习前沿

教学资料

• 教学参考
• 课件
• 小清爱提问：什么是词向量[]
• 小清爱提问：什么是序列到序列模型[]
• 小清爱提问：什么是注意力机制[]
• 小清爱提问：什么是自监督学习[]
• 小清爱提问：什么是对抗生成网络[]
• 小清爱提问：什么是变分自编码器[]

扩展阅读

• AI100问：什么是残差网络[]
• AI100问：什么是词向量[]
• AI100问：什么是序列到序列模型[]
• AI100问：什么是注意力机制[]
• AI100问：什么是自监督学习[]
• AI100问：什么是对抗生成网络[]
• AI100问：什么是变分自编码器[]

演示链接

• Word Embedding [1]

高级读者

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• Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781, 2013. [4]
• Schroff F, Kalenichenko D, Philbin J. Facenet: A unified embedding for face recognition and clustering[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 815-823. [5]
• Li C, Ma X, Jiang B, et al. Deep speaker: an end-to-end neural speaker embedding system[J]. arXiv preprint arXiv:1705.02304, 2017. [6]
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• Liu Y, Liu D, Lv J, et al. Generating Chinese poetry from images via concrete and abstract information[C]//2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020: 1-8. [9]
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• Liu X, Zhang F, Hou Z, et al. Self-supervised learning: Generative or contrastive[J]. IEEE Transactions on Knowledge and Data Engineering, 2021. [14]
• Schneider S, Baevski A, Collobert R, et al. wav2vec: Unsupervised pre-training for speech recognition[J]. arXiv preprint arXiv:1904.05862, 2019. [15]
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• Devlin J, Chang M W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv:1810.04805, 2018. [17]
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• Ethayarajh K. How contextual are contextualized word representations? comparing the geometry of BERT, ELMo, and GPT-2 embeddings[J]. arXiv preprint arXiv:1909.00512, 2019. [19]
• Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[J]. Advances in neural information processing systems, 2014, 27. [20]
• Kingma D P, Welling M. Auto-encoding variational bayes[J]. arXiv preprint arXiv:1312.6114, 2013. [21]
• 王东，机器学习导论,第三章，神经模型，2021,清华大学出版社 [22]
• Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning [23]