Daichi Mochihashi

103 total papers · 755 total citations
46 papers, 474 citations indexed

About

Daichi Mochihashi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Daichi Mochihashi has authored 46 papers receiving a total of 474 indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Artificial Intelligence, 13 papers in Computer Vision and Pattern Recognition and 12 papers in Signal Processing. Recurrent topics in Daichi Mochihashi's work include Topic Modeling (21 papers), Natural Language Processing Techniques (20 papers) and Speech Recognition and Synthesis (8 papers). Daichi Mochihashi is often cited by papers focused on Topic Modeling (21 papers), Natural Language Processing Techniques (20 papers) and Speech Recognition and Synthesis (8 papers). Daichi Mochihashi collaborates with scholars based in Japan, United States and France. Daichi Mochihashi's co-authors include Takeshi Yamada, Naonori Ueda, Takayuki Nagai, Tomoaki Nakamura, Ichiro Kobayashi, Kazuyoshi Yoshii, Ryota Tomioka, Masataka Goto, Eiichiro Sumita and Hirokazu Kameoka and has published in prestigious journals such as Machine Learning, Psychometrika and Communications Biology.

In The Last Decade

Daichi Mochihashi

39 papers receiving 443 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Daichi Mochihashi	338	160	118	31	24	46	474
Vassilis Pitsikalis	157 0.5×	136 0.8×	207 1.8×	23 0.7×	6 0.3×	35	441
Régine Andre-Obrecht	256 0.8×	291 1.8×	113 1.0×	26 0.8×	7 0.3×	50	462
Yuli Xue	229 0.7×	141 0.9×	180 1.5×	16 0.5×	7 0.3×	24	483
Mohammad Mehdi Homayounpour	352 1.0×	270 1.7×	72 0.6×	19 0.6×	32 1.3×	79	528
Jindřich Matoušek	301 0.9×	187 1.2×	56 0.5×	20 0.6×	5 0.2×	70	411
Aristodemos Pnevmatikakis	89 0.3×	117 0.7×	156 1.3×	25 0.8×	34 1.4×	58	422
Masahide Kaneko	75 0.2×	71 0.4×	381 3.2×	59 1.9×	12 0.5×	89	496
Hayato Kobayashi	256 0.8×	166 1.0×	105 0.9×	14 0.5×	56 2.3×	31	443
K. Sengupta	118 0.3×	121 0.8×	341 2.9×	20 0.6×	19 0.8×	33	495
Yingming Li	263 0.8×	49 0.3×	335 2.8×	13 0.4×	21 0.9×	54	550

Countries citing papers authored by Daichi Mochihashi

Since Specialization

Citations

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

Fields of papers citing papers by Daichi Mochihashi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daichi Mochihashi

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

All Works

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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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