| Learning from few examples: A summary of approaches to few-shot learning A Parnami, M Lee arXiv preprint arXiv:2203.04291, 2022 | 123 | 2022 |
| Few-shot keyword spotting with prototypical networks A Parnami, M Lee 2022 7th International Conference on Machine Learning Technologies (ICMLT …, 2022 | 28 | 2022 |
| Pruning attention heads of transformer models using a* search: A novel approach to compress big nlp architectures A Parnami, R Singh, T Joshi arXiv preprint arXiv:2110.15225, 2021 | 5 | 2021 |
| Deep learning based urban analytics platform: Applications to traffic flow modeling and prediction A Parnami, P Bavi, D Papanikolaou, S Akella, M Lee, S Krishnan ACM SIGKDD Workshop on Mining Urban Data (MUD3); ACM: London, UK, 2018 | 5 | 2018 |
| Transformation of node to knowledge graph embeddings for faster link prediction in social networks A Parnami, M Deshpande, AK Mishra, M Lee arXiv preprint arXiv:2111.09308, 2021 | 3 | 2021 |
| Privacy enhancement for cloud-based few-shot learning A Parnami, M Usama, L Fan, M Lee 2022 International Joint Conference on Neural Networks (IJCNN), 1-10, 2022 | 2 | 2022 |
| Rethinking Few-Shot Learning for Speech, Continual Learning and Privacy A Parnami The University of North Carolina at Charlotte, 2022 | | 2022 |
Minwoo LeeAssistant Professor of Computer Science, UNC CharlotteVerified email at uncc.edu
