├── README.md └── notes ├── alternative-structure-char-rnn.md ├── attention-with-intention.md ├── batch-normalization.md ├── character-aware-nlm.md ├── character-level-cnn-for-text-classification.md ├── clstm-large-scale.md ├── deep-knowledge-tracing.md ├── deep-residual-learning.md ├── distilling-the-knowledge-in-a-nn.md ├── distributed-representations-of-sentences-and-documents.md ├── diversity-promoting-objective-ncm.md ├── document-embedding-with-pv.md ├── e2e-dialog-ghnnm.md ├── efficient-char-level-document-classification-cnn-rnn.md ├── end-to-end-memory-networks.md ├── exploring-the-limits-of-lm.md ├── grammar-as-a-foreign-language.md ├── larger-context-lm.md ├── learning-document-embeddings-ngrams.md ├── learning-phrase-representations.md ├── learning-to-execute.md ├── multi-way-nmt-shared-attention.md ├── multilingual-language-processing-from-bytes.md ├── multitask-seq2seq.md ├── neural-attention-model-for-abstractive-sentence-summarization.md ├── neural-conversational-model.md ├── neural-responding-machine.md ├── neural-turing-machines.md ├── nmt-jointly-learning-to-align-and-translate.md ├── nn-context-sentitive-responses.md ├── on-using-very-large-target-vocabulary-for-nmt.md ├── pointer-networks.md ├── properties-of-neural-mt.md ├── recurrent-models-of-visual-attention.md ├── renet-rnn-alternative-to-convnet.md ├── rmn-language-modeling.md ├── rnn-regularization.md ├── semi-supervised-sequence-learning.md ├── sensitivity-analysis-cnn-sentence-classification.md ├── seq2seq-with-neural-networks.md ├── show-attend-tell.md ├── skip-thought-vectors.md ├── spatial-transformer-networks.md ├── stochastic-depth.md ├── strategies-for-training-large-vocab-lm.md ├── target-dependent-sentiment-lstm.md ├── teaching-machines-to-read-and-comprehend.md ├── text-understanding-from-scratch.md ├── training-very-deep-networks.md ├── unified-tagging-blstm.md └── webnav.md /README.md: 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