| The Flores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation | 0 | 0.34 | 2022 |
| Efficient Continual Learning with Modular Networks and Task-Driven Priors | 0 | 0.34 | 2021 |
| Residual Energy-Based Models For Text | 0 | 0.34 | 2021 |
| The Source-Target Domain Mismatch Problem in Machine Translation | 0 | 0.34 | 2021 |
| Revisiting Self-Training for Neural Sequence Generation | 4 | 0.40 | 2020 |
| On The Evaluation of Machine Translation SystemsTrained With Back-Translation. | 1 | 0.36 | 2020 |
| Task-Driven Modular Networks For Zero-Shot Compositional Learning | 0 | 0.34 | 2019 |
| Mixture Models for Diverse Machine Translation: Tricks of the Trade. | 1 | 0.35 | 2019 |
| Multiple-Attribute Text Rewriting. | 0 | 0.34 | 2019 |
| Real or Fake? Learning to Discriminate Machine from Human Generated Text. | 0 | 0.34 | 2019 |
| Large Memory Layers with Product Keys | 1 | 0.37 | 2019 |
| Facebook AI's WAT19 Myanmar-English Translation Task Submission. | 0 | 0.34 | 2019 |
| Large-Scale Pollen Recognition With Deep Learning | 0 | 0.34 | 2019 |
| The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English. | 0 | 0.34 | 2019 |
| Analyzing Uncertainty in Neural Machine Translation. | 14 | 0.56 | 2018 |
| Multiple-Attribute Text Style Transfer. | 4 | 0.37 | 2018 |
| Lightweight Adaptive Mixture of Neural and N-gram Language Models. | 1 | 0.35 | 2018 |
| Classical Structured Prediction Losses for Sequence to Sequence Learning. | 16 | 0.82 | 2018 |
| Phrase-Based & Neural Unsupervised Machine Translation. | 22 | 0.63 | 2018 |
| Efficient Lifelong Learning with A-GEM. | 6 | 0.42 | 2018 |
| Transformation-Based Models of Video Sequences. | 10 | 0.53 | 2017 |
| Unsupervised Machine Translation Using Monolingual Corpora Only. | 40 | 1.03 | 2017 |
| Training Language Models Using Target-Propagation. | 1 | 0.39 | 2017 |
| Fader Networks: Manipulating Images by Sliding Attributes. | 46 | 1.27 | 2017 |
| Gradient Episodic Memory for Continuum Learning. | 41 | 1.13 | 2017 |
| Learning through Dialogue Interactions by Asking Questions | 11 | 0.49 | 2017 |
| Word Translation Without Parallel Data. | 40 | 0.93 | 2017 |
| Dialogue Learning With Human-In-The-Loop. | 0 | 0.34 | 2016 |
| Learning Through Dialogue Interactions. | 0 | 0.34 | 2016 |
| Sequence Level Training with Recurrent Neural Networks | 192 | 4.79 | 2015 |
| Guest Editorial: Deep Learning | 3 | 0.38 | 2015 |
| DeepFace: Closing the Gap to Human-Level Performance in Face Verification | 1323 | 48.35 | 2014 |
| On Learning Where To Look. | 17 | 2.49 | 2014 |
| Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews. | 26 | 1.33 | 2014 |
| Video (language) modeling: a baseline for generative models of natural videos. | 79 | 3.77 | 2014 |
| Predicting Parameters in Deep Learning. | 155 | 9.82 | 2013 |
| PANDA: Pose Aligned Networks for Deep Attribute Modeling | 155 | 25.75 | 2013 |
| On rectified linear units for speech processing | 113 | 9.13 | 2013 |
| Multi-GPU Training of ConvNets. | 15 | 5.79 | 2013 |
| Modeling Natural Images Using Gated MRFs | 25 | 1.65 | 2013 |
| An Empirical Study Of Learning Rates In Deep Neural Networks For Speech Recognition | 40 | 3.17 | 2013 |
| Learning Factored Representations in a Deep Mixture of Experts. | 14 | 0.90 | 2013 |
| DeViSE: A Deep Visual-Semantic Embedding Model. | 397 | 16.73 | 2013 |
| On Autoencoders and Score Matching for Energy Based Models. | 38 | 2.61 | 2011 |
| On deep generative models with applications to recognition | 90 | 3.48 | 2011 |
| Generating more realistic images using gated MRF's. | 24 | 1.56 | 2010 |
| Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. | 89 | 19.43 | 2010 |
| Modeling pixel means and covariances using factorized third-order boltzmann machines | 108 | 10.17 | 2010 |
| Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images | 99 | 9.46 | 2010 |
| Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition | 103 | 16.48 | 2010 |
