The llama 3 herd of models A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... arXiv preprint arXiv:2407.21783, 2024 | 2275 | 2024 |
Federated learning with buffered asynchronous aggregation J Nguyen, K Malik, H Zhan, A Yousefpour, M Rabbat, M Malek, D Huba International Conference on Artificial Intelligence and Statistics, 3581-3607, 2022 | 328 | 2022 |
Active federated learning J Goetz, K Malik, D Bui, S Moon, H Liu, A Kumar arXiv preprint arXiv:1909.12641, 2019 | 195 | 2019 |
Federated learning with partial model personalization K Pillutla, K Malik, AR Mohamed, M Rabbat, M Sanjabi, L Xiao International Conference on Machine Learning, 17716-17758, 2022 | 182 | 2022 |
Effective long-context scaling of foundation models W Xiong, J Liu, I Molybog, H Zhang, P Bhargava, R Hou, L Martin, ... arXiv preprint arXiv:2309.16039, 2023 | 172 | 2023 |
Papaya: Practical, private, and scalable federated learning D Huba, J Nguyen, K Malik, R Zhu, M Rabbat, A Yousefpour, CJ Wu, ... Proceedings of Machine Learning and Systems 4, 814-832, 2022 | 150 | 2022 |
Federated user representation learning D Bui, K Malik, J Goetz, H Liu, S Moon, A Kumar, KG Shin arXiv preprint arXiv:1909.12535, 2019 | 101 | 2019 |
Where to begin? on the impact of pre-training and initialization in federated learning J Nguyen, J Wang, K Malik, M Sanjabi, M Rabbat arXiv preprint arXiv:2206.15387, 2022 | 77 | 2022 |
Personalized Federated Learning for Assistant Systems K Malik, S Moon, LIU Honglei, A Kumar, H Zhan, A Aly US Patent App. 16/815,990, 2021 | 74 | 2021 |
Towards next-generation intelligent assistants leveraging llm techniques XL Dong, S Moon, YE Xu, K Malik, Z Yu Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 71 | 2023 |
The llama 3 herd of models A Grattafiori, A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, ... arXiv e-prints, arXiv: 2407.21783, 2024 | 69 | 2024 |
Fedsynth: Gradient compression via synthetic data in federated learning S Hu, J Goetz, K Malik, H Zhan, Z Liu, Y Liu arXiv preprint arXiv:2204.01273, 2022 | 42 | 2022 |
Where to begin? exploring the impact of pre-training and initialization in federated learning J Nguyen, K Malik, M Sanjabi, M Rabbat arXiv preprint arXiv:2206.15387 4, 2022 | 41 | 2022 |
Voice-based Auto-Completions and Auto-Responses for Assistant Systems F Botros, N Wang, F Wang, MPD EDIZ, O Muzaffar, K Malik, ... US Patent App. 17/120,013, 2022 | 31 | 2022 |
Exploiting postdominance for speculative parallelization M Agarwal, K Malik, KM Woley, SS Stone, MI Frank 2007 IEEE 13th International Symposium on High Performance Computer …, 2007 | 27 | 2007 |
Paco: Probability-based path confidence prediction K Malik, M Agarwal, V Dhar, MI Frank 2008 IEEE 14th International Symposium on High Performance Computer …, 2008 | 18 | 2008 |
Fetch-criticality reduction through control independence M Agarwal, N Navale, K Malik, MI Frank ACM SIGARCH Computer Architecture News 36 (3), 13-24, 2008 | 11 | 2008 |
Active federated learning. arXiv 2019 J Goetz, K Malik, D Bui, S Moon, H Liu, A Kumar arXiv preprint arXiv:1909.12641, 0 | 11 | |
Branch-mispredict level parallelism (BLP) for control independence K Malik, M Agarwal, SS Stone, KM Woley, MI Frank 2008 IEEE 14th International Symposium on High Performance Computer …, 2008 | 8 | 2008 |
Task execution based on real-world text detection for assistant systems EK Santoro, D Savenkov, KHG Goh, K Malik, R Srivastava US Patent 12,125,297, 2024 | 4 | 2024 |