Jeremy Bernstein
- Artificial Intelligence
- Computer Networks and Communications
- Numerical Analysis
- Computational Mechanics
- Computational Theory and Mathematics
- Co-authors
- Kamyar AzizzadenesheliAnima AnandkumarIshita DasguptaDavid RolnickYu-Xiang WangHaim Sompolinsky
- Topics
- Stochastic Gradient Optimization Techniques (3 papers)Advanced Memory and Neural Computing (1 paper)Markov Chains and Monte Carlo Methods (1 paper)
- Journals
- arXiv (Cornell University)International Conference on Learning RepresentationsNational Conference on Artificial Intelligence
- Partner nations
- United States
In The Last Decade
Jeremy Bernstein
4 papers receiving 13 citations
Peers
Comparison fields: 5 of 16
- Artificial Intelligence 9
- Computer Networks and Communications 3
- Numerical Analysis 3
- Computational Mechanics 3
- Computational Theory and Mathematics 2
Countries citing papers authored by Jeremy Bernstein
This map shows the geographic impact of Jeremy Bernstein'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 Jeremy Bernstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeremy Bernstein more than expected).
Fields of papers citing papers by Jeremy Bernstein
This network shows the impact of papers produced by Jeremy Bernstein. 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 Jeremy Bernstein. The network helps show where Jeremy Bernstein may publish in the future.
Co-authorship network of co-authors of Jeremy Bernstein
This figure shows the co-authorship network connecting the top 25 collaborators of Jeremy Bernstein. A scholar is included among the top collaborators of Jeremy Bernstein 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 Jeremy Bernstein. Jeremy Bernstein is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant | 7 |
| 2 | Compression by the signs: distributed learning is a two-way street | 2 |
| 3 | Convergence rate of sign stochastic gradient descent for non-convex functions | 2 |
| 4 | Markov Transitions between Attractor States in a Recurrent Neural Network. | 2 |
About Jeremy Bernstein
Jeremy Bernstein is a scholar working on Artificial Intelligence, Statistics and Probability and Computational Theory and Mathematics, having authored 4 papers that have together received 13 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (3 papers), Advanced Memory and Neural Computing (1 paper) and Markov Chains and Monte Carlo Methods (1 paper). The work is most often cited by research in Numerical Analysis (3 citations), Computer Graphics and Computer-Aided Design (1 citation) and Artificial Intelligence (9 citations). Jeremy Bernstein has collaborated with scholars based in United States. Frequent co-authors include Kamyar Azizzadenesheli, Anima Anandkumar, Ishita Dasgupta, David Rolnick, Yu-Xiang Wang and Haim Sompolinsky. Their work appears in journals such as arXiv (Cornell University), International Conference on Learning Representations and National Conference on Artificial Intelligence.
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.