Daichi Mochihashi
- Artificial Intelligence top 5%
- Signal Processing top 5%
- Computer Vision and Pattern Recognition top 10%
- Control and Systems Engineering
- Cognitive Neuroscience
- Co-authors
- Takeshi YamadaNaonori UedaTakayuki NagaiTomoaki NakamuraIchiro KobayashiMasataka GotoKazuyoshi YoshiiRyota Tomioka
- Topics
- Topic Modeling (21 papers)Natural Language Processing Techniques (20 papers)Speech Recognition and Synthesis (7 papers)
- Partner nations
- JapanUnited StatesFrance
In The Last Decade
Daichi Mochihashi
40 papers receiving 448 citations
Peers
Comparison fields: 5 of 63
- Artificial Intelligence 339
- Signal Processing 160
- Computer Vision and Pattern Recognition 120
- Control and Systems Engineering 31
- Cognitive Neuroscience 24
Countries citing papers authored by Daichi Mochihashi
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 2 | |
| 9 | 11 | |
| 10 | MIPA: Mutual Information Based Paraphrase Acquisition via Bilingual Pivoting | 1 |
| 11 | 34 | |
| 12 | 1 | |
| 13 | Infinite Positive Semidefinite Tensor Factorization for Source Separation of Mixture Signals | 30 |
| 14 | Learning Common Grammar from Multilingual Corpus | 6 |
| 15 | The Infinite Markov Model | 19 |
| 16 | The niCT-ATR statistical machine translation system for the IWSLT 2006 evaluation. | 13 |
| 17 | Bayesian approaches in Natural Language Processing | 2 |
| 18 | Context as Filtering | 6 |
| 19 | Learning Nonstructural Distance Metric by Minimum Cluster Distortion. | 8 |
| 20 | Probabilistic Representation of Meanings | 3 |
About Daichi Mochihashi
Daichi Mochihashi is a scholar working on Computational Mathematics, Artificial Intelligence and Signal Processing, having authored 46 papers that have together received 479 indexed citations. Recurring topics across this work include Topic Modeling (21 papers), Natural Language Processing Techniques (20 papers) and Speech Recognition and Synthesis (7 papers). The work is most often cited by research in Signal Processing (160 citations), Artificial Intelligence (339 citations) and Computational Mathematics (4 citations). Daichi Mochihashi has collaborated with scholars based in Japan, United States and France. Frequent co-authors include Takeshi Yamada, Naonori Ueda, Takayuki Nagai, Tomoaki Nakamura, Ichiro Kobayashi, Masataka Goto, Kazuyoshi Yoshii, Ryota Tomioka, Eiichiro Sumita and Hideki Asoh. Their work appears in journals such as Machine Learning, Psychometrika and Communications Biology.
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.