Yasumasa Onoe

492 total citations
12 papers, 202 citations indexed

About

Yasumasa Onoe is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Yasumasa Onoe has authored 12 papers receiving a total of 202 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 1 paper in Molecular Biology. Recurrent topics in Yasumasa Onoe's work include Topic Modeling (10 papers), Natural Language Processing Techniques (6 papers) and Multimodal Machine Learning Applications (4 papers). Yasumasa Onoe is often cited by papers focused on Topic Modeling (10 papers), Natural Language Processing Techniques (6 papers) and Multimodal Machine Learning Applications (4 papers). Yasumasa Onoe collaborates with scholars based in United States and Japan. Yasumasa Onoe's co-authors include Greg Durrett, Andrew McCallum, Eunsol Choi, Michael Zhang, Radu Soricut, Stefano Pellegrini, William Chan, Sarah Laszlo, Chitwan Saharia and David J. Fleet and has published in prestigious journals such as Proceedings of the AAAI Conference on Artificial Intelligence and Journal of Natural Language Processing.

In The Last Decade

Yasumasa Onoe

10 papers receiving 189 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yasumasa Onoe United States	7	128	91	19	17	12	12	202
Weichong Yin China	5	174 1.4×	195 2.1×	3 0.2×	12 0.7×	11 0.9×	6	262
Yuning Jiang China	7	131 1.0×	216 2.4×	8 0.4×	36 2.1×	8 0.7×	18	268
Sosuke Kobayashi Japan	8	119 0.9×	86 0.9×	14 0.7×	14 0.8×	8 0.7×	15	187
Leigang Qu China	7	152 1.2×	253 2.8×	5 0.3×	21 1.2×	7 0.6×	11	308
Yawen Zeng China	10	107 0.8×	222 2.4×	14 0.7×	28 1.6×		23	287
Qiang Ning United States	8	155 1.2×	55 0.6×	22 1.2×	20 1.2×		10	194
Anastasia Pentina Austria	4	160 1.3×	109 1.2×	9 0.5×	4 0.2×	1 0.1×	5	205
Tongzheng Ren United States	4	54 0.4×	72 0.8×	2 0.1×	7 0.4×	18 1.5×	10	121
Aditay Tripathi India	3	265 2.1×	61 0.7×	31 1.6×	34 2.0×		7	293
Belinda Zeng United States	6	181 1.4×	176 1.9×	7 0.4×	13 0.8×		13	264

Countries citing papers authored by Yasumasa Onoe

Since Specialization

Citations

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

Fields of papers citing papers by Yasumasa Onoe

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yasumasa Onoe

This figure shows the co-authorship network connecting the top 25 collaborators of Yasumasa Onoe. A scholar is included among the top collaborators of Yasumasa Onoe 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 Yasumasa Onoe. Yasumasa Onoe 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.

Onoe, Yasumasa, et al.. (2024). Jamp_sp : A Controlled Japanese Temporal Inference Dataset Considering Aspect. Journal of Natural Language Processing. 31(2). 637–679.

2.

Burns, Andrea, Burcu Karagol Ayan, Yasumasa Onoe, et al.. (2024). ImageInWords: Unlocking Hyper-Detailed Image Descriptions. 93–127. 1 indexed citations

3.

Onoe, Yasumasa, et al.. (2023). Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge. 5469–5485. 18 indexed citations

4.

Wang, Su, Chitwan Saharia, Jordi Pont-Tuset, et al.. (2023). Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting. 18359–18369. 66 indexed citations

5.

Onoe, Yasumasa, et al.. (2023). Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models. 57–68. 1 indexed citations

6.

Onoe, Yasumasa, et al.. (2022). Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection. 705–715. 9 indexed citations

7.

Onoe, Yasumasa, et al.. (2022). Intermediate Entity-based Sparse Interpretable Representation Learning. 210–224. 1 indexed citations

8.

Onoe, Yasumasa, Michael Zhang, Eunsol Choi, & Greg Durrett. (2022). Entity Cloze By Date: What LMs Know About Unseen Entities. 693–702. 14 indexed citations

9.

Onoe, Yasumasa, et al.. (2021). Modeling Fine-Grained Entity Types with Box Embeddings. 2051–2064. 32 indexed citations

10.

Durrett, Greg, Jifan Chen, Shrey Desai, et al.. (2021). Contemporary NLP Modeling in Six Comprehensive Programming Assignments. 99–103.

11.

Onoe, Yasumasa & Greg Durrett. (2020). Interpretable Entity Representations through Large-Scale Typing. 612–624. 11 indexed citations

12.

Onoe, Yasumasa & Greg Durrett. (2020). Fine-Grained Entity Typing for Domain Independent Entity Linking. Proceedings of the AAAI Conference on Artificial Intelligence. 34(5). 8576–8583. 49 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.

Explore authors with similar magnitude of impact