Daichi Mochihashi

756 total citations
46 papers, 479 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 479 indexed citations (citations by other indexed papers that have themselves been cited), including 38 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 (7 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 (7 papers). Daichi Mochihashi collaborates with scholars based in Japan, United States and France. Daichi Mochihashi's co-authors include Takeshi Yamada, Naonori Ueda, Tomoaki Nakamura, Ichiro Kobayashi, Takayuki Nagai, Kazuyoshi Yoshii, Ryota Tomioka, Masataka Goto, Eiichiro Sumita and Hideki Asoh and has published in prestigious journals such as Machine Learning, Psychometrika and Communications Biology.

In The Last Decade

Daichi Mochihashi

40 papers receiving 448 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daichi Mochihashi Japan 10 339 160 120 31 24 46 479
Hideki Kashioka Japan 15 546 1.6× 180 1.1× 64 0.5× 24 0.8× 24 1.0× 97 637
Christian Fügen United States 14 526 1.6× 183 1.1× 85 0.7× 47 1.5× 12 0.5× 36 651
Stephen E. Levinson United States 9 136 0.4× 72 0.5× 43 0.4× 15 0.5× 15 0.6× 33 275
Björn Hoffmeister Germany 18 715 2.1× 489 3.1× 82 0.7× 24 0.8× 26 1.1× 38 850
Yossi Adi Israel 15 661 1.9× 395 2.5× 121 1.0× 12 0.4× 36 1.5× 56 838
Katunobu Itou Japan 12 410 1.2× 362 2.3× 89 0.7× 19 0.6× 38 1.6× 65 661
Xu Tan China 23 914 2.7× 398 2.5× 400 3.3× 21 0.7× 47 2.0× 61 1.1k
Neil Zeghidour United States 8 237 0.7× 177 1.1× 77 0.6× 12 0.4× 12 0.5× 17 377
Jaesung Huh South Korea 11 425 1.3× 378 2.4× 57 0.5× 7 0.2× 14 0.6× 24 556
Damien Vincent France 8 249 0.7× 218 1.4× 273 2.3× 13 0.4× 16 0.7× 12 551

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

20 of 20 papers shown
1.
Mimura, Koki, Jumpei Matsumoto, Daichi Mochihashi, et al.. (2024). Unsupervised decomposition of natural monkey behavior into a sequence of motion motifs. Communications Biology. 7(1). 1080–1080. 3 indexed citations
3.
Mochihashi, Daichi, et al.. (2023). Scale-Invariant Infinite Hierarchical Topic Model. 1 indexed citations
4.
Taniguchi, Tadahiro, et al.. (2023). Holographic CCG Parsing. 262–276. 1 indexed citations
5.
Mochihashi, Daichi, et al.. (2023). Dynamical Non-compensatory Multidimensional IRT Model Using Variational Approximation. Psychometrika. 88(2). 487–526. 1 indexed citations
6.
Komachi, Mamoru, et al.. (2022). Infinite SCAN: An Infinite Model of Diachronic Semantic Change. 1605–1616. 1 indexed citations
7.
Shinohara, Shigeko, et al.. (2021). Articulation of geminate obstruents in the Ikema dialect of Miyako Ryukyuan: A real-time MRI analysis. Journal of the International Phonetic Association. 53(1). 69–93.
8.
Nakamura, Tomoaki, et al.. (2019). HVGH: Unsupervised Segmentation for High-Dimensional Time Series Using Deep Neural Compression and Statistical Generative Model. Frontiers in Robotics and AI. 6. 115–115. 11 indexed citations
9.
Kajiwara, Tomoyuki, Mamoru Komachi, & Daichi Mochihashi. (2017). MIPA: Mutual Information Based Paraphrase Acquisition via Bilingual Pivoting. International Joint Conference on Natural Language Processing. 1. 80–89. 1 indexed citations
10.
Nakamura, Tomoaki, et al.. (2015). Learning Word Meanings and Grammar for Describing Everyday Activities in Smart Environments. 2249–2254. 1 indexed citations
11.
Yoshii, Kazuyoshi, Ryota Tomioka, Daichi Mochihashi, & Masataka Goto. (2013). Infinite Positive Semidefinite Tensor Factorization for Source Separation of Mixture Signals. International Conference on Machine Learning. 576–584. 30 indexed citations
12.
Mochihashi, Daichi, et al.. (2012). Predicting Word Fixations in Text with a CRF Model for Capturing General Reading Strategies among Readers. 55–70. 8 indexed citations
13.
Watanabe, Shinji, Daichi Mochihashi, Takaaki Hori, & Atsushi Nakamura. (2011). Gibbs sampling based Multi-scale Mixture Model for speaker clustering. 2. 4524–4527. 7 indexed citations
14.
Iwata, Tomoharu, Daichi Mochihashi, & Hiroshi Sawada. (2010). Learning Common Grammar from Multilingual Corpus. Meeting of the Association for Computational Linguistics. 184–188. 6 indexed citations
15.
Mochihashi, Daichi & Eiichiro Sumita. (2007). The Infinite Markov Model. Neural Information Processing Systems. 20. 1017–1024. 19 indexed citations
16.
Mochihashi, Daichi. (2006). Bayesian approaches in Natural Language Processing. IEICE Technical Report; IEICE Tech. Rep.. 106(279). 25–30. 2 indexed citations
17.
Zhang, Ruiqiang, Hirofumi Yamamoto, Michael Paul, et al.. (2006). The niCT-ATR statistical machine translation system for the IWSLT 2006 evaluation.. IWSLT. 83–90. 13 indexed citations
18.
Mochihashi, Daichi & Yūji Matsumoto. (2005). Context as Filtering. Neural Information Processing Systems. 18. 907–914. 6 indexed citations
19.
Mochihashi, Daichi, Genichiro Kikui, & Kenji Kita. (2004). Learning Nonstructural Distance Metric by Minimum Cluster Distortion.. Empirical Methods in Natural Language Processing. 341–348. 8 indexed citations
20.
Mochihashi, Daichi & Yūji Matsumoto. (2002). Probabilistic Representation of Meanings. IPSJ SIG Notes. 2002(4). 77–84. 3 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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