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

• WordEmbedding [1]
• HoggingFace 演示[2]

高级读者

• He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.[3]
• Bengio Y, Ducharme R, Vincent P. A neural probabilistic language model[J]. Advances in neural information processing systems, 2000, 13. [4]
• Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781, 2013. [5]
• 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. [6]
• 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. [7]
• Lin Y, Liu Z, Sun M, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Twenty-ninth AAAI conference on artificial intelligence. 2015. [8]
• Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks[J]. Advances in neural information processing systems, 2014, 27. [9]
• 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. [10]
• Sun D, Ren T, Li C, et al. Learning to write stylized chinese characters by reading a handful of examples[J]. arXiv preprint arXiv:1712.06424, 2017. [11]
• Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv:1409.0473, 2014. [12]
• Xu K, Ba J, Kiros R, et al. Show, attend and tell: Neural image caption generation with visual attention[C]//International conference on machine learning. PMLR, 2015: 2048-2057. [13]
• Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30. [14]
• Liu X, Zhang F, Hou Z, et al. Self-supervised learning: Generative or contrastive[J]. IEEE Transactions on Knowledge and Data Engineering, 2021. [15]
• Schneider S, Baevski A, Collobert R, et al. wav2vec: Unsupervised pre-training for speech recognition[J]. arXiv preprint arXiv:1904.05862, 2019. [16]
• Noroozi M, Favaro P. Unsupervised learning of visual representations by solving jigsaw puzzles[C]//European conference on computer vision. Springer, Cham, 2016: 69-84. [17]
• 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. [18]
• Brown T, Mann B, Ryder N, et al. Language models are few-shot learners[J]. Advances in neural information processing systems, 2020, 33: 1877-1901. [19]
• Radford A, Wu J, Child R, et al. Language models are unsupervised multitask learners[J]. OpenAI blog, 2019, 1(8): 9.[]
• 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. [20]
• Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[J]. Advances in neural information processing systems, 2014, 27. [21]
• Kingma D P, Welling M. Auto-encoding variational bayes[J]. arXiv preprint arXiv:1312.6114, 2013. [22]
• 王东，机器学习导论,第三章，神经模型，2021,清华大学出版社 [23]
• Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning [24]