Vladimir Braverman
- Computer Networks and Communications top 5%
- Artificial Intelligence top 5%
- Information Systems top 10%
- Electrical and Electronic Engineering
- Hardware and Architecture top 10%
- Co-authors
- Zaoxing LiuZhuolong YuVyas SekarRoy FriedmanGil EinzigerRan Ben BasatXin JinYaron Kassner
- Topics
- Sparse and Compressive Sensing Techniques (8 papers)Complexity and Algorithms in Graphs (8 papers)Data Management and Algorithms (7 papers)
- Partner nations
- United StatesIsraelSwitzerland
In The Last Decade
Vladimir Braverman
47 papers receiving 513 citations
Peers
Comparison fields: 5 of 59
- Computer Networks and Communications 390
- Artificial Intelligence 208
- Information Systems 94
- Electrical and Electronic Engineering 61
- Hardware and Architecture 52
Countries citing papers authored by Vladimir Braverman
This map shows the geographic impact of Vladimir Braverman'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 Vladimir Braverman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vladimir Braverman more than expected).
Fields of papers citing papers by Vladimir Braverman
This network shows the impact of papers produced by Vladimir Braverman. 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 Vladimir Braverman. The network helps show where Vladimir Braverman may publish in the future.
Co-authorship network of co-authors of Vladimir Braverman
This figure shows the co-authorship network connecting the top 25 collaborators of Vladimir Braverman. A scholar is included among the top collaborators of Vladimir Braverman based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vladimir Braverman. Vladimir Braverman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | The Benefits of Implicit Regularization from SGD in Least Squares Problems | 1 |
| 6 | 8 | |
| 7 | Data-Independent Neural Pruning via Coresets | 3 |
| 8 | On the noisy gradient descent that generalizes as SGD | 1 |
| 9 | 5 | |
| 10 | Streaming coreset constructions for M-estimators | 4 |
| 11 | On Activation Function Coresets for Network Pruning | 1 |
| 12 | I Know What You Did Last Summer Network Monitoring using Interval Queries | 2 |
| 13 | 5 | |
| 14 | The Physical Systems Behind Optimization Algorithms | 2 |
| 15 | Differentially Private Robust Low-Rank Approximation | 3 |
| 16 | Dynamic Factorization and Partition of Complex Networks. | 2 |
| 17 | Dynamic Partition of Complex Networks | 1 |
| 18 | 8 | |
| 19 | 8 | |
| 20 | 7 |
About Vladimir Braverman
Vladimir Braverman is a scholar working on Computer Graphics and Computer-Aided Design, Signal Processing and Artificial Intelligence, having authored 50 papers that have together received 520 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (8 papers), Complexity and Algorithms in Graphs (8 papers) and Data Management and Algorithms (7 papers). The work is most often cited by research in Computer Networks and Communications (390 citations), Hardware and Architecture (52 citations) and Artificial Intelligence (208 citations). Vladimir Braverman has collaborated with scholars based in United States, Israel and Switzerland. Frequent co-authors include Zaoxing Liu, Zhuolong Yu, Vyas Sekar, Roy Friedman, Gil Einziger, Ran Ben Basat, Xin Jin, Yaron Kassner, Mosharaf Chowdhury and David P. Woodruff. Their work appears in journals such as Scientific Reports, Sensors and IEEE Transactions on Neural Networks and Learning Systems.
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.