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Neural Networks : the Official Journal of the International Neural Network Society
|
February 11, 2003
Learning to generate articulated behavior through the bottom-up and the top-down interaction processes
Jun Tani
Frontiers in Neurorobotics
|
October 30, 2008
On the interactions between top-down anticipation and bottom-up regression
Jun Tani
Neural Computation
|
September 17, 2019
A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition
Ahmadreza Ahmadi, Jun Tani
Plos Computational Biology
|
November 8, 2008
Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment
Yuichi Yamashita, Jun Tani
Neural Networks : the Official Journal of the International Neural Network Society
|
October 22, 2008
A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance
Jun Namikawa, Jun Tani
Neural Networks : the Official Journal of the International Neural Network Society
|
October 27, 2015
Development of compositional and contextual communicable congruence in robots by using dynamic neural network models
Gibeom Park, Jun Tani
Frontiers in Neurorobotics
|
October 5, 2020
Investigation of the Sense of Agency in Social Cognition, Based on Frameworks of Predictive Coding and Active Inference: A Simulation Study on Multimodal Imitative Interaction
Wataru Ohata, Jun Tani
Frontiers in Neurorobotics
|
October 3, 2022
Initialization of latent space coordinates <i>via</i> random linear projections for learning robotic sensory-motor sequences
Vsevolod Nikulin, Jun Tani
Neural Networks : the Official Journal of the International Neural Network Society
|
August 18, 2004
Learning to generate combinatorial action sequences utilizing the initial sensitivity of deterministic dynamical systems
Ryu Nishimoto, Jun Tani
Neural Networks : the Official Journal of the International Neural Network Society
|
January 5, 2010
Learning to imitate stochastic time series in a compositional way by chaos
Jun Namikawa, Jun Tani
Page
of 6
Search research articles
Search
Showing results (1-10 of 56) with videos related to
Sort By:
Page
of 6
Neural Networks : the Official Journal of the International Neural Network Society
|
February 11, 2003
Learning to generate articulated behavior through the bottom-up and the top-down interaction processes
Jun Tani
Frontiers in Neurorobotics
|
October 30, 2008
On the interactions between top-down anticipation and bottom-up regression
Jun Tani
Neural Computation
|
September 17, 2019
A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition
Ahmadreza Ahmadi, Jun Tani
Plos Computational Biology
|
November 8, 2008
Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment
Yuichi Yamashita, Jun Tani
Neural Networks : the Official Journal of the International Neural Network Society
|
October 22, 2008
A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance
Jun Namikawa, Jun Tani
Neural Networks : the Official Journal of the International Neural Network Society
|
October 27, 2015
Development of compositional and contextual communicable congruence in robots by using dynamic neural network models
Gibeom Park, Jun Tani
Frontiers in Neurorobotics
|
October 5, 2020
Investigation of the Sense of Agency in Social Cognition, Based on Frameworks of Predictive Coding and Active Inference: A Simulation Study on Multimodal Imitative Interaction
Wataru Ohata, Jun Tani
Frontiers in Neurorobotics
|
October 3, 2022
Initialization of latent space coordinates <i>via</i> random linear projections for learning robotic sensory-motor sequences
Vsevolod Nikulin, Jun Tani
Neural Networks : the Official Journal of the International Neural Network Society
|
August 18, 2004
Learning to generate combinatorial action sequences utilizing the initial sensitivity of deterministic dynamical systems
Ryu Nishimoto, Jun Tani
Neural Networks : the Official Journal of the International Neural Network Society
|
January 5, 2010
Learning to imitate stochastic time series in a compositional way by chaos
Jun Namikawa, Jun Tani
Page
of 6