Ross Goroshin

3.3k total citations · 1 hit paper
8 papers, 1.1k citations indexed

About

Ross Goroshin is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Oceanography. According to data from OpenAlex, Ross Goroshin has authored 8 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 2 papers in Oceanography. Recurrent topics in Ross Goroshin's work include Generative Adversarial Networks and Image Synthesis (3 papers), Image and Signal Denoising Methods (2 papers) and Underwater Acoustics Research (2 papers). Ross Goroshin is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (3 papers), Image and Signal Denoising Methods (2 papers) and Underwater Acoustics Research (2 papers). Ross Goroshin collaborates with scholars based in United States, France and United Kingdom. Ross Goroshin's co-authors include Yann LeCun, Jonathan Tompson, Christoph Bregler, Arjun Jain, Raia Hadsell, Piotr Mirowski, Andrea Banino, Dharshan Kumaran, Razvan Pascanu and Andrew J. Ballard and has published in prestigious journals such as arXiv (Cornell University), Neural Information Processing Systems and International Conference on Learning Representations.

In The Last Decade

Ross Goroshin

8 papers receiving 1.0k citations

Hit Papers

align trajectories sort by overperformance

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.

Efficient object localization using Convolutional Networks

2015 805 citations Jonathan Tompson, Ross Goroshin et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ross Goroshin United States	5	664	421	100	91	77	8	1.1k
Yinghao Xu China	15	1.0k 1.5×	343 0.8×	109 1.1×	97 1.1×	48 0.6×	54	1.4k
Jie Nie China	18	815 1.2×	350 0.8×	125 1.3×	43 0.5×	58 0.8×	124	1.3k
Wen Zheng China	19	646 1.0×	193 0.5×	62 0.6×	145 1.6×	62 0.8×	91	1.6k
Chun-Fu Richard Chen United States	6	797 1.2×	395 0.9×	115 1.1×	40 0.4×	104 1.4×	12	1.4k
Zhanjie Song China	20	688 1.0×	175 0.4×	163 1.6×	96 1.1×	40 0.5×	88	1.2k
Longlong Jing United States	11	824 1.2×	749 1.8×	131 1.3×	51 0.6×	81 1.1×	17	1.6k
Minh Hoai United States	21	1.2k 1.9×	490 1.2×	92 0.9×	153 1.7×	23 0.3×	60	1.5k
Dhanesh Ramachandram Malaysia	13	345 0.5×	397 0.9×	73 0.7×	30 0.3×	35 0.5×	39	992
Lin Qi China	12	459 0.7×	164 0.4×	111 1.1×	69 0.8×	98 1.3×	116	821
Qinghe Zheng China	17	477 0.7×	423 1.0×	84 0.8×	26 0.3×	121 1.6×	39	1.3k

Countries citing papers authored by Ross Goroshin

Since Specialization

Citations

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

Fields of papers citing papers by Ross Goroshin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ross Goroshin

This figure shows the co-authorship network connecting the top 25 collaborators of Ross Goroshin. A scholar is included among the top collaborators of Ross Goroshin 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 Ross Goroshin. Ross Goroshin 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:

8 of 8 papers shown

1.

Dumoulin, Vincent, Neil Houlsby, Utku Evci, et al.. (2021). A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches. Neural Information Processing Systems. 2 indexed citations

2.

Mirowski, Piotr, Razvan Pascanu, Fabio Viola, et al.. (2016). Learning to Navigate in Complex Environments. arXiv (Cornell University). 173 indexed citations

3.

Tompson, Jonathan, Ross Goroshin, Arjun Jain, Yann LeCun, & Christoph Bregler. (2015). Efficient object localization using Convolutional Networks. 648–656. 805 indexed citations breakdown →

4.

Goroshin, Ross, Joan Bruna, Jonathan Tompson, David Eigen, & Yann LeCun. (2015). Unsupervised Feature Learning from Temporal Data. International Conference on Learning Representations. 2 indexed citations

5.

Goroshin, Ross, Joan Bruna, Jonathan Tompson, David Eigen, & Yann LeCun. (2015). Unsupervised Learning of Spatiotemporally Coherent Metrics. 4086–4093. 62 indexed citations

6.

Goroshin, Ross, Michaël Mathieu, & Yann LeCun. (2015). Learning to Linearize Under Uncertainty. arXiv (Cornell University). 28. 1234–1242. 17 indexed citations

7.

Goroshin, Ross, et al.. (2010). Automated cable tracking in sonar imagery. 7 indexed citations

8.

Goroshin, Ross, et al.. (2009). Automated cable detection in sonar imagery. 2745–2750. 1 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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