| Residual Energy-Based Models For Text | 0 | 0.34 | 2021 |
| Sequence-to-Lattice Models for Fast Translation. | 0 | 0.34 | 2021 |
| Algorithm-Hardware Co-Design of Adaptive Floating-Point Encodings for Resilient Deep Learning Inference | 3 | 0.46 | 2020 |
| Residual Energy-Based Models for Text Generation | 0 | 0.34 | 2020 |
| Cascaded Text Generation with Markov Transformers | 0 | 0.34 | 2020 |
| Challenges in End-to-End Neural Scientific Table Recognition. | 0 | 0.34 | 2019 |
| Real or Fake? Learning to Discriminate Machine from Human Generated Text. | 0 | 0.34 | 2019 |
| Neural Linguistic Steganography | 1 | 0.38 | 2019 |
| OpenNMT: Neural Machine Translation Toolkit. | 1 | 0.35 | 2018 |
| Visual Attention Model for Cross-sectional Stock Return Prediction and End-to-End Multimodal Market Representation Learning. | 0 | 0.34 | 2018 |
| Bottom-Up Abstractive Summarization. | 0 | 0.34 | 2018 |
| Latent Alignment and Variational Attention. | 7 | 0.43 | 2018 |
| Image-to-Markup Generation with Coarse-to-Fine Attention. | 12 | 0.62 | 2017 |
| Opennmt: Open-Source Toolkit For Neural Machine Translation | 134 | 4.01 | 2017 |
| Dropout with Expectation-linear Regularization. | 6 | 0.43 | 2017 |
| Learning Latent Space Models with Angular Constraints. | 1 | 0.35 | 2017 |
| Neural Machine Translation with Recurrent Attention Modeling. | 9 | 0.52 | 2016 |
| Learning Concept Taxonomies From Multi-Modal Data | 6 | 0.43 | 2016 |
| What You Get Is What You See: A Visual Markup Decompiler. | 5 | 0.67 | 2016 |
| Latent Variable Modeling with Diversity-Inducing Mutual Angular Regularization | 3 | 0.40 | 2015 |
| Entity Hierarchy Embedding | 19 | 0.73 | 2015 |
| Diversifying Restricted Boltzmann Machine for Document Modeling | 27 | 0.77 | 2015 |
| Creating Scalable and Interactive Web Applications Using High Performance Latent Variable Models | 1 | 0.39 | 2015 |
| On the Generalization Error Bounds of Neural Networks under Diversity-Inducing Mutual Angular Regularization. | 6 | 0.47 | 2015 |
