Dan Alistarh

4.4k citations
86 papers · 732 · h-index 14

Impact in

Papers in

Dan Alistarh

73 papers receiving 705 citations

Peers

Dan Alistarh
Comparison fields: 5 of 59
  • Hardware and Architecture 213
  • Computer Networks and Communications 430
  • Artificial Intelligence 334
  • Computer Vision and Pattern Recognition 158
  • Computational Mathematics 2
Replace Michaela Blott with:
Michaela Blott United States
Dimitrios Prountzos United States
Tarek S. Abdelrahman Canada
Roman Manevich United States
Kishore Kothapalli India
Tae Jun Ham South Korea
Ammar Ahmad Awan United States
Bor-Yiing Su United States
Flemming Friche Rodler Denmark
Youwei Zhuo United States
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Citations per field
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Citations per year

Countries citing papers authored by Dan Alistarh

Since Specialization
Citations

This map shows the geographic impact of Dan Alistarh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dan Alistarh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Alistarh more than expected).

Fields of papers citing papers by Dan Alistarh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dan Alistarh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dan Alistarh. The network helps show where Dan Alistarh may publish in the future.

Co-authors

The 25 scholars most cited alongside Dan Alistarh, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dan Alistarh Line = papers co-authored together Dan Alistarh links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 86 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201958
2 201556
3
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning
201752
4 201750
5 201439
6
Byzantine Stochastic Gradient Descent
201838
7
QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent
201635
8 201524
9
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks
202022
10 201520
11 201518
12 201714
13 201514
14 201413
15 202013
16 202211
17 201811
18 201111
19 201810
20 201610

About Dan Alistarh

Dan Alistarh is a scholar working on Computer Networks and Communications, Artificial Intelligence, Hardware and Architecture, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 86 papers that have together received 732 indexed citations. Recurring topics across this work include Distributed systems and fault tolerance (39 papers), Optimization and Search Problems (23 papers), Parallel Computing and Optimization Techniques (23 papers), Stochastic Gradient Optimization Techniques (18 papers), Advanced Neural Network Applications (10 papers), Sparse and Compressive Sensing Techniques (9 papers), Privacy-Preserving Technologies in Data (6 papers) and Cryptography and Data Security (5 papers). The work is most often cited by research in Hardware and Architecture (213 citations), Computer Networks and Communications (430 citations), Artificial Intelligence (334 citations), Computer Vision and Pattern Recognition (158 citations) and Computational Mathematics (2 citations). Dan Alistarh has collaborated with scholars based in Austria, United States and Switzerland. Frequent co-authors include Jerry Li, Nir Shavit, Milan Vojnović, Kaan Kara, Alexander Matveev, Rati Gelashvili, Seth Gilbert, Cédric Renggli, Torsten Hoefler and Zeyuan Allen-Zhu. Their work appears in journals such as Distributed Computing, ACM SIGPLAN Notices, Journal of the ACM, ACM SIGCOMM Computer Communication Review and Algorithmica.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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