Nathan Silberman

10.4k total citations · 3 hit papers
12 papers, 2.0k citations indexed

About

Nathan Silberman is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Nathan Silberman has authored 12 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Nathan Silberman's work include Generative Adversarial Networks and Image Synthesis (4 papers), Advanced Vision and Imaging (2 papers) and Machine Learning and Data Classification (2 papers). Nathan Silberman is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (4 papers), Advanced Vision and Imaging (2 papers) and Machine Learning and Data Classification (2 papers). Nathan Silberman collaborates with scholars based in United States, United Kingdom and Switzerland. Nathan Silberman's co-authors include Dilip Krishnan, Konstantinos Bousmalis, David Dohan, Dumitru Erhan, Rob Fergus, Sergio Guadarrama, Kevin Murphy, George Papandreou, Vivek Rathod and Jonathan Huang and has published in prestigious journals such as International Journal of Computer Vision, arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Nathan Silberman

12 papers receiving 1.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

2017 942 citations Konstantinos Bousmalis, Nathan Silberman et al. profile →
Indoor scene segmentation using a structured light sensor

2011 326 citations Nathan Silberman, Rob Fergus profile →
Im2Calories: Towards an Automated Mobile Vision Food Diary

2015 320 citations Austin Myers, Nick Johnston et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Nathan Silberman United States	9	1.2k	790	242	204	184	12	2.0k
Qianru Sun Singapore	22	1.7k 1.4×	1.6k 2.0×	205 0.8×	193 0.9×	50 0.3×	60	2.6k
Zifeng Wu China	10	1.5k 1.3×	544 0.7×	678 2.8×	162 0.8×	36 0.2×	26	2.3k
Christian Micheloni Italy	28	1.8k 1.5×	649 0.8×	522 2.2×	44 0.2×	123 0.7×	130	2.6k
Stefano Cagnoni Italy	23	522 0.4×	631 0.8×	315 1.3×	59 0.3×	116 0.6×	116	1.8k
Vijay Vasudevan United States	17	1.2k 1.0×	1.1k 1.4×	69 0.3×	142 0.7×	17 0.1×	34	3.4k
Harpreet Sawhney United States	33	5.3k 4.4×	710 0.9×	159 0.7×	48 0.2×	148 0.8×	113	6.3k
Li Song China	25	1.5k 1.2×	261 0.3×	95 0.4×	53 0.3×	29 0.2×	242	2.1k
Thomas Mensink Netherlands	21	2.3k 1.9×	1.1k 1.4×	112 0.5×	87 0.4×	11 0.1×	49	2.8k
David Page United States	22	918 0.8×	348 0.4×	82 0.3×	56 0.3×	15 0.1×	107	2.1k
Rameswar Panda United States	18	1.4k 1.2×	663 0.8×	152 0.6×	177 0.9×	6 0.0×	56	2.1k

Countries citing papers authored by Nathan Silberman

Since Specialization

Citations

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

Fields of papers citing papers by Nathan Silberman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nathan Silberman

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

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Gerrard, Paul, Jingbin Zhang, Rikiya Yamashita, et al.. (2024). Analytical Validation of a Clinical Grade Prognostic and Classification Artificial Intelligence Laboratory Test for Men with Prostate Cancer. 1(2). 119–126. 3 indexed citations

2.

Lovchinsky, Igor, Pouya Samangouei, Ardavan Saeedi, et al.. (2020). Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth. International Conference on Learning Representations. 3 indexed citations

3.

Tanno, Ryutaro, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, & Nathan Silberman. (2019). Learning From Noisy Labels by Regularized Estimation of Annotator Confusion. 11236–11245. 129 indexed citations

4.

Wojna, Zbigniew, Vittorio Ferrari, Sergio Guadarrama, et al.. (2019). The Devil is in the Decoder: Classification, Regression and GANs. International Journal of Computer Vision. 127(11-12). 1694–1706. 49 indexed citations

5.

Bousmalis, Konstantinos, Nathan Silberman, David Dohan, Dumitru Erhan, & Dilip Krishnan. (2017). Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. 95–104. 942 indexed citations breakdown →

6.

Silberman, Nathan. (2017). TF-Slim: A Lightweight Library for Defining, Training and Evaluating Complex Models in TensorFlow. SMARTech Repository (Georgia Institute of Technology). 11 indexed citations

7.

Wojna, Zbigniew, Jasper Uijlings, Sergio Guadarrama, et al.. (2017). The Devil is in the Decoder. arXiv (Cornell University). 11 indexed citations

8.

Myers, Austin, Nick Johnston, Vivek Rathod, et al.. (2015). Im2Calories: Towards an Automated Mobile Vision Food Diary. 1233–1241. 320 indexed citations breakdown →

9.

Silberman, Nathan & Rob Fergus. (2011). Indoor scene segmentation using a structured light sensor. 326 indexed citations breakdown →

10.

Silberman, Nathan, et al.. (2010). Case for automated detection of diabetic retinopathy. National Conference on Artificial Intelligence. 85–90. 41 indexed citations

11.

McDonald, Ryan, et al.. (2009). Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models. Neural Information Processing Systems. 22. 1231–1239. 137 indexed citations

12.

Silberman, Nathan, et al.. (2009). On the rise and fall of ISPs. 4 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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