Hadi Daneshmand
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
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- Stochastic Gradient Optimization Techniques
- Domain Adaptation and Few-Shot Learning
- Machine Learning and Algorithms
Papers in
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- Stochastic Gradient Optimization Techniques 3
- Neural Networks and Applications 2
- Machine Learning and Algorithms 1
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- Complex Network Analysis Techniques 2
- Opinion Dynamics and Social Influence 2
- Co-authors
- Aurélien Lucchi (6 shared papers)Thomas Hofmann (6 shared papers)Manuel Gomez-Rodriguez (2 shared papers)Le Song (2 shared papers)Jonas Köhler (4 shared papers)Bernhard Schölkopf (1 shared paper)Klaus Neymeyr (1 shared paper)Francis Bach (2 shared papers)
- Journals
- Journal of Machine Learning Research (1 paper)PubMed (1 paper)International Conference on Machine Learning (1 paper)Repository for Publications and Research Data (ETH Zurich) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- SwitzerlandUnited StatesGermany
In The Last Decade
Hadi Daneshmand
8 papers receiving 94 citations
Peers
Comparison fields: 5 of 41
- Statistical and Nonlinear Physics 40
- Artificial Intelligence 55
- Acoustics and Ultrasonics 1
- Computer Vision and Pattern Recognition 20
- Statistics and Probability 7
Countries citing papers authored by Hadi Daneshmand
This map shows the geographic impact of Hadi Daneshmand'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 Hadi Daneshmand with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hadi Daneshmand more than expected).
Fields of papers citing papers by Hadi Daneshmand
This network shows the impact of papers produced by Hadi Daneshmand. 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 Hadi Daneshmand. The network helps show where Hadi Daneshmand may publish in the future.
Co-authors
The 9 scholars most cited alongside Hadi Daneshmand, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm. | 2014 | 31 |
| 2 | Estimating diffusion networks: recovery conditions, sample complexity & soft-thresholding algorithm | 2016 | 14 |
| 3 | Towards a Theoretical Understanding of Batch Normalization. | 2018 | 14 |
| 4 | 2019 | 13 | |
| 5 | Batch normalization provably avoids ranks collapse for randomly initialised deep networks | 2020 | 9 |
| 6 | 2016 | 7 | |
| 7 | Escaping Saddles with Stochastic Gradients | 2018 | 6 |
| 8 | Theoretical Understanding of Batch-normalization: A Markov Chain Perspective. | 2020 | 3 |
| 9 | 2023 | 0 | |
| 10 | 2023 | 0 |
About Hadi Daneshmand
Hadi Daneshmand is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Statistics and Probability, Computational Mechanics and Numerical Analysis, having authored 10 papers that have together received 97 indexed citations. Recurring topics across this work include Markov Chains and Monte Carlo Methods (3 papers), Stochastic Gradient Optimization Techniques (3 papers), Sparse and Compressive Sensing Techniques (2 papers), Complex Network Analysis Techniques (2 papers), Neural Networks and Applications (2 papers), Opinion Dynamics and Social Influence (2 papers), Machine Learning and Algorithms (1 paper) and Digital Filter Design and Implementation (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (40 citations), Artificial Intelligence (55 citations), Acoustics and Ultrasonics (1 citation), Computer Vision and Pattern Recognition (20 citations) and Statistics and Probability (7 citations). Hadi Daneshmand has collaborated with scholars based in Switzerland, United States and Germany. Frequent co-authors include Aurélien Lucchi, Thomas Hofmann, Manuel Gomez-Rodriguez, Le Song, Jonas Köhler, Bernhard Schölkopf, Klaus Neymeyr, Francis Bach and Suvrit Sra. Their work appears in journals such as Journal of Machine Learning Research, PubMed, International Conference on Machine Learning, Repository for Publications and Research Data (ETH Zurich) and Neural Information Processing 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.