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.
Deep learning, reinforcement learning, and world models
2022264 citationsEiji Uchibe, Jun Morimoto et al.Neural Networksprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Jun Morimoto'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 Jun Morimoto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Morimoto more than expected).
This network shows the impact of papers produced by Jun Morimoto. 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 Jun Morimoto. The network helps show where Jun Morimoto may publish in the future.
Co-authorship network of co-authors of Jun Morimoto
This figure shows the co-authorship network connecting the top 25 collaborators of Jun Morimoto.
A scholar is included among the top collaborators of Jun Morimoto 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 Jun Morimoto. Jun Morimoto is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Miyazaki, Hisashi, Jun Morimoto, Kohji Toda, Shinobu Onoda, & Takeshi Ohshima. (2011). Characterization of LiNbO. Japanese Journal of Applied Physics. 50(7).1 indexed citations
13.
Matsubara, Takamitsu, Sang-Ho Hyon, & Jun Morimoto. (2010). A Learning Method for Nonlinear Dynamical Motor Primitives from a Variety of Nominal Trajectories : Application to Robot Learning from Demonstration. IEICE technical report. Speech. 110(265). 251–256.1 indexed citations
14.
Matsubara, Takamitsu, et al.. (2010). Adaptive Step-size Policy Gradients with Average Reward Metric. Asian Conference on Machine Learning. 285–298.4 indexed citations
Endo, Gen, Jun Morimoto, Takamitsu Matsubara, Jun Nakanishi, & Gordon Cheng. (2005). Learning CPG sensory feedback with policy gradient for biped locomotion for a full-body humanoid. National Conference on Artificial Intelligence. 1267–1273.28 indexed citations
17.
Nakanishi, Jun, Jun Morimoto, Gen Endo, et al.. (2003). Learning from demonstration and adaptation of biped locomotion with dynamical movement primitives.23 indexed citations
18.
Morimoto, Jun, et al.. (1999). Studies of relationship between deep levels and RoA product in mesa type HgCdTe devices. Opto-Electronics Review. 361–367.3 indexed citations
19.
Morimoto, Jun & Kenji Doya. (1998). Hierarchical Reinforcement Learning of Low-Dimensional Subgoals and High-Dimensional Trajectories. International Conference on Neural Information Processing. 850–853.17 indexed citations
20.
Tachibana, Masayoshi, et al.. (1977). Estimation of cochlear blood flow by direct plasma space counting:effects of Ifenprodil. 70(11). 1603–1611.1 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.