| What do we mean by generalization in federated learning? H Yuan, W Morningstar, L Ning, K Singhal arXiv preprint arXiv:2110.14216, 2021 | 58 | 2021 |
| Deep reuse: Streamline CNN inference on the fly via coarse-grained computation reuse L Ning, X Shen Proceedings of the ACM International Conference on Supercomputing, 438-448, 2019 | 38 | 2019 |
| Adaptive deep reuse: Accelerating cnn training on the fly L Ning, H Guan, X Shen 2019 IEEE 35th International Conference on Data Engineering (ICDE), 1538-1549, 2019 | 34 | 2019 |
| In-place zero-space memory protection for cnn H Guan, L Ning, Z Lin, X Shen, H Zhou, SH Lim Advances in Neural Information Processing Systems 32, 2019 | 25 | 2019 |
| LCD: A fast contrastive divergence based algorithm for restricted Boltzmann machine L Ning, R Pittman, X Shen Neural Networks 108, 399-410, 2018 | 21 | 2018 |
| Learning federated representations and recommendations with limited negatives L Ning, K Singhal, EX Zhou, S Prakash arXiv preprint arXiv:2108.07931, 2021 | 12 | 2021 |
| General reuse-centric CNN accelerator NM Cicek, L Ning, O Ozturk, X Shen IEEE Transactions on Computers 71 (4), 880-891, 2021 | 10 | 2021 |
| Generalizations of the theory and deployment of triangular inequality for compiler-based strength reduction Y Ding, L Ning, H Guan, X Shen Proceedings of the 38th ACM SIGPLAN Conference on Programming Language …, 2017 | 9 | 2017 |
| EANA: reducing privacy risk on large-scale recommendation models L Ning, S Chien, S Song, M Chen, Y Xue, D Berlowitz Proceedings of the 16th ACM Conference on Recommender Systems, 399-407, 2022 | 6 | 2022 |
| Mixed federated learning: Joint decentralized and centralized learning S Augenstein, A Hard, L Ning, K Singhal, S Kale, K Partridge, R Mathews arXiv preprint arXiv:2205.13655, 2022 | 6 | 2022 |
| Simple augmentation goes a long way: Adrl for dnn quantization L Ning, G Chen, W Zhang, X Shen International Conference on Learning Representations, 2020 | 6 | 2020 |
| Deep Reuse for Deep Learning L Ning North Carolina State University, 2020 | 2 | 2020 |
| POSTER: Cutting the Fat: Speeding Up RBM for Fast Deep Learning Through Generalized Redundancy Elimination L Ning, R Pittman, X Shen 2017 26th International Conference on Parallel Architectures and Compilation …, 2017 | 2 | 2017 |
| User-LLM: Efficient LLM Contextualization with User Embeddings L Ning, L Liu, J Wu, N Wu, D Berlowitz, S Prakash, B Green, S O'Banion, ... arXiv preprint arXiv:2402.13598, 2024 | 1 | 2024 |
| Recurrent neural networks meet context-free grammar: Two birds with one stone H Guan, U Chaudhary, Y Xu, L Ning, L Zhang, X Shen 2021 IEEE International Conference on Data Mining (ICDM), 1078-1083, 2021 | 1 | 2021 |
| Generalization of Teacher-Student Network and CNN Pruning T Menzies, SH Lim, H Guan, X Shen, L Ning North Carolina State University. Dept. of Computer Science, 2018 | 1 | 2018 |
| System (s) and method (s) for jointly learning machine learning model (s) based on server data and client data S Augenstein, A Hard, K Partridge, R Mathews, L Ning, K Singhal US Patent App. 17/848,947, 2023 | | 2023 |
| EANA: Reducing Privacy Risk on Large-scale Recommendation Models D Berlowitz, L Ning, M Chen, QQ Xue, S Song, S Chien | | 2022 |
| A Just-In-Time Compiler for Intelligent Manufacturing T Menzies, SH Lim, X Shen, L Ning, H Guan North Carolina State University. Dept. of Computer Science, 2018 | | 2018 |
| LCD: A fast contrastive divergence based training algorithm for restricted Boltzmann machine X Shen, L Ning North Carolina State University. Dept. of Computer Science, 2016 | | 2016 |
