| Accelerating slide deep learning on modern cpus: Vectorization, quantizations, memory optimizations, and more S Daghaghi, N Meisburger, M Zhao, A Shrivastava Proceedings of Machine Learning and Systems 3, 156-166, 2021 | 34 | 2021 |
| A tale of two efficient and informative negative sampling distributions S Daghaghi, T Medini, N Meisburger, B Chen, M Zhao, A Shrivastava International conference on machine learning, 2319-2329, 2021 | 11 | 2021 |
| Distributed SLIDE: Enabling training large neural networks on low bandwidth and simple CPU-clusters via model parallelism and sparsity M Yan, N Meisburger, T Medini, A Shrivastava arXiv preprint arXiv:2201.12667, 2022 | 6 | 2022 |
| Distributed tera-scale similarity search with MPI: provably efficient similarity search over billions without a single distance computation N Meisburger, A Shrivastava arXiv preprint arXiv:2008.03260, 2020 | 4 | 2020 |
| BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Neural Networks on Commodity CPU Hardware N Meisburger, V Lakshman, B Geordie, J Engels, DT Ramos, P Pranav, ... arXiv preprint arXiv:2303.17727, 2023 | 3 | 2023 |
| From Research to Production: Towards Scalable and Sustainable Neural Recommendation Models on Commodity CPU Hardware A Shrivastava, V Lakshman, T Medini, N Meisburger, J Engels, ... Proceedings of the 17th ACM Conference on Recommender Systems, 1071-1074, 2023 | 1 | 2023 |
| BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware N Meisburger, V Lakshman, B Geordie, J Engels, DT Ramos, P Pranav, ... Proceedings of the 32nd ACM International Conference on Information and …, 2023 | | 2023 |
