Techniques

Training Deeper Neural Machine Translation Models with Transparent Attention EMNLP 2018 Training Deep Neural Networks with 8-bit Floating Point Numbers NeurIPS 2018 Training Neural Networks Using Features Replay NeurIPS 2018 Training wide residual networks for deployment using a single bit for each weight ICLR 2018 Transformation Networks for Target-Oriented Sentiment Classification ACL 2018 Two Local Models for Neural Constituent Parsing COLING 2018 Understanding Batch Normalization NeurIPS 2018 Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units NeurIPS 2018 Uni-DUE Student Team: Tackling fact checking through decomposable attention neural network EMNLP 2018 Using Deep Neural Networks for Identification of Slavic Languages from Acoustic Signal Interspeech 2018 Using Intermediate Representations to Solve Math Word Problems ACL 2018 Using Prosodic and Lexical Information for Learning Utterance-level Behaviors in Psychotherapy Interspeech 2018 Video Person Re-Identification With Competitive Snippet-Similarity Aggregation and Co-Attentive Snippet Embedding CVPR 2018 Virtual Class Enhanced Discriminative Embedding Learning NeurIPS 2018 Visual Attention Model for Name Tagging in Multimodal Social Media ACL 2018 Wavelet Pooling for Convolutional Neural Networks ICLR 2018 Weightless: Lossy weight encoding for deep neural network compression ICML 2018 When does deep multi-task learning work for loosely related document classification tasks? EMNLP 2018 Where to Prune: Using LSTM to Guide End-to-end Pruning IJCAI 2018 Wide Compression: Tensor Ring Nets CVPR 2018 YNU_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension SemEval 2018 YNU-HPCC at SemEval-2018 Task 2: Multi-ensemble Bi-GRU Model with Attention Mechanism for Multilingual Emoji Prediction SemEval 2018 A Cognition Based Attention Model for Sentiment Analysis EMNLP 2017 Action Unit Detection With Region Adaptation, Multi-Labeling Learning and Optimal Temporal Fusing CVPR 2017 Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples NeurIPS 2017