| QSGD: Communication-efficient SGD via gradient quantization and encoding D Alistarh, D Grubic, J Li, R Tomioka, M Vojnovic Advances in neural information processing systems 30, 2017 | 1414 | 2017 |
| Model compression via distillation and quantization A Polino, R Pascanu, D Alistarh ICLR 2018, 2018 | 618 | 2018 |
| The convergence of sparsified gradient methods D Alistarh, T Hoefler, M Johansson, N Konstantinov, S Khirirat, C Renggli Advances in Neural Information Processing Systems 31, 2018 | 414 | 2018 |
| Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks T Hoefler, D Alistarh, T Ben-Nun, N Dryden, A Peste The Journal of Machine Learning Research 22 (1), 10882-11005, 2021 | 309 | 2021 |
| Byzantine stochastic gradient descent D Alistarh, Z Allen-Zhu, J Li Advances in Neural Information Processing Systems 31, 2018 | 245 | 2018 |
| ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning H Zhang, J Li, K Kara, D Alistarh, J Liu, C Zhang International Conference on Machine Learning, 4035-4043, 2017 | 204* | 2017 |
| The spraylist: A scalable relaxed priority queue D Alistarh, J Kopinsky, J Li, N Shavit Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of …, 2015 | 133 | 2015 |
| Time-space trade-offs in population protocols D Alistarh, J Aspnes, D Eisenstat, R Gelashvili, RL Rivest Proceedings of the twenty-eighth annual ACM-SIAM symposium on discrete …, 2017 | 132 | 2017 |
| Fast and exact majority in population protocols D Alistarh, R Gelashvili, M Vojnović Proceedings of the 2015 ACM Symposium on Principles of Distributed Computing …, 2015 | 118 | 2015 |
| SparCML: High-performance sparse communication for machine learning C Renggli, S Ashkboos, M Aghagolzadeh, D Alistarh, T Hoefler Proceedings of the International Conference for High Performance Computing …, 2019 | 110 | 2019 |
| Space-optimal majority in population protocols D Alistarh, J Aspnes, R Gelashvili Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018 | 108 | 2018 |
| Polylogarithmic-time leader election in population protocols D Alistarh, R Gelashvili Automata, Languages, and Programming: 42nd International Colloquium, ICALP …, 2015 | 103 | 2015 |
| Woodfisher: Efficient second-order approximation for neural network compression SP Singh, D Alistarh Advances in Neural Information Processing Systems 33, 18098-18109, 2020 | 87 | 2020 |
| FPGA-accelerated dense linear machine learning: A precision-convergence trade-off K Kara, D Alistarh, G Alonso, O Mutlu, C Zhang 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom …, 2017 | 81 | 2017 |
| Inducing and exploiting activation sparsity for fast inference on deep neural networks M Kurtz, J Kopinsky, R Gelashvili, A Matveev, J Carr, M Goin, W Leiserson, ... International Conference on Machine Learning, 5533-5543, 2020 | 76 | 2020 |
| Tight bounds for asynchronous renaming D Alistarh, J Aspnes, K Censor-Hillel, S Gilbert, R Guerraoui Journal of the ACM (JACM) 61 (3), 1-51, 2014 | 71* | 2014 |
| Fast randomized test-and-set and renaming D Alistarh, H Attiya, S Gilbert, A Giurgiu, R Guerraoui Distributed Computing: 24th International Symposium, DISC 2010, Cambridge …, 2010 | 62 | 2010 |
| Stacktrack: An automated transactional approach to concurrent memory reclamation D Alistarh, P Eugster, M Herlihy, A Matveev, N Shavit Proceedings of the Ninth European Conference on Computer Systems, 1-14, 2014 | 61 | 2014 |
| Distributed learning over unreliable networks C Yu, H Tang, C Renggli, S Kassing, A Singla, D Alistarh, C Zhang, J Liu International Conference on Machine Learning, 7202-7212, 2019 | 55 | 2019 |
| Are lock-free concurrent algorithms practically wait-free? D Alistarh, K Censor-Hillel, N Shavit Journal of the ACM (JACM) 63 (4), 1-20, 2016 | 51 | 2016 |
Jerry LiMicrosoft ResearchVerified email at microsoft.com
