| Gibbs max-margin topic models with data augmentation J Zhu, N Chen, H Perkins, B Zhang The Journal of Machine Learning Research 15 (1), 1073-1110, 2014 | 104 | 2014 |
| Gibbs max-margin topic models with fast sampling algorithms J Zhu, N Chen, H Perkins, B Zhang International Conference on Machine Learning, 124-132, 2013 | 59 | 2013 |
| Dialog Intent Induction with Deep Multi-View Clustering H Perkins, Y Yang Empirical Methods in Natural Language Processing, 2019 | 43 | 2019 |
| CUDA-on-CL: a compiler and runtime for running NVIDIAŽ CUDA™ C++ 11 applications on OpenCL™ 1.2 Devices H Perkins Proceedings of the 5th International Workshop on OpenCL, 1-4, 2017 | 13 | 2017 |
| cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL H Perkins arXiv preprint arXiv:1606.04884, 2016 | 13 | 2016 |
| Neural networks can understand compositional functions that humans do not, in the context of emergent communication H Perkins arXiv e-prints, arXiv: 2103.04180, 2021 | 5 | 2021 |
| Fast Parallel SVM using Data Augmentation H Perkins, M Xu, J Zhu, B Zhang arXiv preprint arXiv:1512.07716, 2015 | 4 | 2015 |
| Automated conversation goal discovery using neural networks and deep multi-view clustering Y Yang, HN Perkins US Patent 11,687,730, 2023 | 3 | 2023 |
| TexRel: a Green Family of Datasets for Emergent Communications on Relations H Perkins arXiv preprint arXiv:2105.12804, 2021 | 3 | 2021 |
| Icy: A benchmark for measuring compositional inductive bias of emergent communication models H Perkins | | 2021 |
| Compositionality Through Language Transmission, using Artificial Neural Networks H Perkins arXiv preprint arXiv:2101.11739, 2021 | | 2021 |
