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122 Publications

2020 | Conference Paper | IST-REx-ID: 7605 | OA
In search of the fastest concurrent union-find algorithm
D.-A. Alistarh, A. Fedorov, N. Koval, in:, 23rd International Conference on Principles of Distributed Systems, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020, p. 15:1-15:16.
[Published Version] View | Files available | DOI | arXiv
 
2020 | Conference Paper | IST-REx-ID: 7803 | OA
Simple, deterministic, constant-round coloring in the congested clique
A. Czumaj, P. Davies, M. Parter, in:, Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2020, pp. 309–318.
[Submitted Version] View | Files available | DOI | arXiv
 
2020 | Conference Paper | IST-REx-ID: 8725 | OA
The splay-list: A distribution-adaptive concurrent skip-list
V. Aksenov, D.-A. Alistarh, A. Drozdova, A. Mohtashami, in:, 34th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020, p. 3:1-3:18.
[Published Version] View | Files available | DOI | arXiv
 
2020 | Conference Paper | IST-REx-ID: 9632 | OA
WoodFisher: Efficient second-order approximation for neural network compression
S.P. Singh, D.-A. Alistarh, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 18098–18109.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2020 | Conference Paper | IST-REx-ID: 9631 | OA
Scalable belief propagation via relaxed scheduling
V. Aksenov, D.-A. Alistarh, J. Korhonen, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 22361–22372.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2020 | Conference Paper | IST-REx-ID: 9415 | OA
Inducing and exploiting activation sparsity for fast neural network inference
M. Kurtz, J. Kopinsky, R. Gelashvili, A. Matveev, J. Carr, M. Goin, W. Leiserson, S. Moore, B. Nell, N. Shavit, D.-A. Alistarh, in:, 37th International Conference on Machine Learning, ICML 2020, 2020, pp. 5533–5543.
[Published Version] View | Files available
 
2020 | Journal Article | IST-REx-ID: 8268 | OA
Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications
N.M. Gurel, K. Kara, A. Stojanov, T. Smith, T. Lemmin, D.-A. Alistarh, M. Puschel, C. Zhang, IEEE Transactions on Signal Processing 68 (2020) 4268–4282.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2020 | Conference Paper | IST-REx-ID: 8722 | OA
Taming unbalanced training workloads in deep learning with partial collective operations
S. Li, T.B.-N. Tal Ben-Nun, S.D. Girolamo, D.-A. Alistarh, T. Hoefler, in:, Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2020, pp. 45–61.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2020 | Journal Article | IST-REx-ID: 7224 | OA
Habitat fragmentation and species diversity in competitive communities
J. Rybicki, N. Abrego, O. Ovaskainen, Ecology Letters 23 (2020) 506–517.
[Published Version] View | Files available | DOI | WoS
 
2020 | Conference Paper | IST-REx-ID: 8724 | OA
On the sample complexity of adversarial multi-source PAC learning
N.H. Konstantinov, E. Frantar, D.-A. Alistarh, C. Lampert, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 5416–5425.
[Published Version] View | Files available | arXiv
 

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