暂无分享,去创建一个
[1] Janet M. Baker,et al. The Design for the Wall Street Journal-based CSR Corpus , 1992, HLT.
[2] Jürgen Schmidhuber,et al. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks , 2006, ICML.
[3] Edouard Grave,et al. Adaptive Attention Span in Transformers , 2019, ACL.
[4] Kevin Gimpel,et al. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations , 2019, ICLR.
[5] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[6] Matthias Sperber,et al. Self-Attentional Acoustic Models , 2018, INTERSPEECH.
[7] Guillaume Lample,et al. Large Memory Layers with Product Keys , 2019, NeurIPS.
[8] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[9] Xu Tan,et al. MASS: Masked Sequence to Sequence Pre-training for Language Generation , 2019, ICML.
[10] Lukasz Kaiser,et al. Reformer: The Efficient Transformer , 2020, ICLR.
[11] Yiming Yang,et al. Transformer-XL: Attentive Language Models beyond a Fixed-Length Context , 2019, ACL.
[12] Alec Radford,et al. Improving Language Understanding by Generative Pre-Training , 2018 .
[13] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .
[14] Yiming Yang,et al. XLNet: Generalized Autoregressive Pretraining for Language Understanding , 2019, NeurIPS.
[15] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[16] 知秀 柴田. 5分で分かる!? 有名論文ナナメ読み:Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding , 2020 .
[17] Ashish Vaswani,et al. Stand-Alone Self-Attention in Vision Models , 2019, NeurIPS.
[18] Ankit Singh Rawat,et al. Sampled Softmax with Random Fourier Features , 2019, NeurIPS.
[19] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[20] Martin Jaggi,et al. On the Relationship between Self-Attention and Convolutional Layers , 2019, ICLR.
[21] Ilya Sutskever,et al. Generating Long Sequences with Sparse Transformers , 2019, ArXiv.
[22] Joshua Goodman,et al. Classes for fast maximum entropy training , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).
[23] Guy Blanc,et al. Adaptive Sampled Softmax with Kernel Based Sampling , 2017, ICML.
[24] Makoto Yamada,et al. Transformer Dissection: An Unified Understanding for Transformer's Attention via the Lens of Kernel , 2019, EMNLP/IJCNLP.
[25] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[26] Quoc V. Le,et al. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators , 2020, ICLR.
[27] Sepp Hochreiter,et al. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.
[28] Moshe Wasserblat,et al. Q8BERT: Quantized 8Bit BERT , 2019, 2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS Edition (EMC2-NIPS).
[29] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[30] Yoshua Bengio,et al. Hierarchical Probabilistic Neural Network Language Model , 2005, AISTATS.
[31] Omer Levy,et al. Are Sixteen Heads Really Better than One? , 2019, NeurIPS.
[32] Nicolas Usunier,et al. End-to-End Object Detection with Transformers , 2020, ECCV.
[33] Liyuan Liu,et al. On the Variance of the Adaptive Learning Rate and Beyond , 2019, ICLR.
[34] Geoffrey E. Hinton,et al. A Scalable Hierarchical Distributed Language Model , 2008, NIPS.
[35] Omer Levy,et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.
[36] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[37] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[38] Mingyuan Zhang,et al. Efficient Attention: Attention with Linear Complexities. , 2018 .
[39] Xi Chen,et al. PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications , 2017, ICLR.