Niv Giladi

599 total citations
3 papers, 162 citations indexed

About

Niv Giladi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Niv Giladi has authored 3 papers receiving a total of 162 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 1 paper in Computer Vision and Pattern Recognition and 1 paper in Electrical and Electronic Engineering. Recurrent topics in Niv Giladi's work include Machine Learning and Data Classification (2 papers), Stochastic Gradient Optimization Techniques (1 paper) and Neural Networks and Applications (1 paper). Niv Giladi is often cited by papers focused on Machine Learning and Data Classification (2 papers), Stochastic Gradient Optimization Techniques (1 paper) and Neural Networks and Applications (1 paper). Niv Giladi collaborates with scholars based in Israel, Switzerland and United States. Niv Giladi's co-authors include Ethan Fetaya, Dan Levi, Daniel Soudry, Elad Hoffer, Torsten Hoefler, Tal Ben‐Nun and Itay Hubara and has published in prestigious journals such as Sensors, Repository for Publications and Research Data (ETH Zurich) and arXiv (Cornell University).

In The Last Decade

Niv Giladi

3 papers receiving 156 citations

Peers

Niv Giladi

AI

CVPR

MC

CSE

EEE

Tarun Kalluri United States

Kailash J. Karande India

Yixuan Li United States

Luke Metz United States

Sherry Moore United States

Philipp Seidl Austria

Zhengguo Zhu China

Tan M. Nguyen United States

Xiaoyang Qu China

Xu Tan China

Tarun Kalluri United States

Niv Giladi

53 ×0.7

AI

100 ×1.4

CVPR

39 ×1.9

MC

4 ×0.2

CSE

10 ×0.6

EEE

Citations per year, relative to Niv Giladi Niv Giladi (= 1×) peers Tarun Kalluri

Countries citing papers authored by Niv Giladi

Since Specialization

Citations

This map shows the geographic impact of Niv Giladi'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 Niv Giladi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Niv Giladi more than expected).

Fields of papers citing papers by Niv Giladi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Niv Giladi. 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 Niv Giladi. The network helps show where Niv Giladi may publish in the future.

Co-authorship network of co-authors of Niv Giladi

This figure shows the co-authorship network connecting the top 25 collaborators of Niv Giladi. A scholar is included among the top collaborators of Niv Giladi 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 Niv Giladi. Niv Giladi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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3 of 3 papers shown

1.

Levi, Dan, et al.. (2022). Evaluating and Calibrating Uncertainty Prediction in Regression Tasks. Sensors. 22(15). 5540–5540. 71 indexed citations

2.

Giladi, Niv, et al.. (2020). At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?. arXiv (Cornell University). 1 indexed citations

3.

Hoffer, Elad, Tal Ben‐Nun, Itay Hubara, et al.. (2020). Augment Your Batch: Improving Generalization Through Instance Repetition. Repository for Publications and Research Data (ETH Zurich). 8126–8135. 90 indexed citations

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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