Jun Tani

Cognitive Neuroscience · 1998–2018

A researcher in the Science corpus: 12 papers; trusts 243 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 98 citations into it; #2,094 of 10,852 by trust here, #4,616 by citations
• Artificial Intelligence — 80 citations into it; #10,999 of 35,345 by trust here, #12,387 by citations
• Computer Vision and Pattern Recognition — 65 citations into it; #8,590 of 21,575 by trust here, #9,722 by citations
• Neuroscience — 47 citations into it
• Model Architectures — 28 citations into it; #1,384 of 2,278 by trust here, #1,572 by citations
• Social Psychology — 25 citations into it; #4,152 of 15,694 by trust here, #8,310 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Control and Systems Engineering — 19 citations into it; #3,468 of 8,257 by trust here, #5,227 by citations

Whom Jun Tani trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Sepp Hochreiter
3. Michael I. Jordan
4. Paul J. Werbos
5. J. Urgen Schmidhuber
6. Alex Krizhevsky
7. David E. Rumelhart
8. Yann LeCun
9. Yoshua Bengio
10. Allen Newell

Who trusts Jun Tani overall

1. German Ignacio Parisi
2. Frederic Kaplan
3. Pierre-Yves Oudeyer
4. Stefan Wermter
5. Tyler L. Hayes
6. Kenji Doya
7. Yuxuan Lou
8. Sara Hooker
9. Wataru Takano
10. Luiza Mici

Papers from Jun Tani's perspective

Ordered by how strongly the researchers Jun Tani trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization (2018) — 134 citations
• Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment (2008) — 507 citations
• On-line imitative interaction with a humanoid robot using a mirror neuron model (2004) — 15 citations
• Learning to generate articulated behavior through the bottom-up and the top-down interaction processes (2003) — 171 citations
• Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach (2017) — 15 citations
• Lifelong learning of human actions with deep neural network self-organization (2017) — 128 citations
• Emergence of multimodal action representations from neural network self-organization (2017) — 43 citations
• Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena (2016) — 122 citations
• Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences (2015) — 33 citations
• Imitating others by composition of primitive actions: A neuro-dynamic model (2012) — 26 citations
• Open-end human–robot interaction from the dynamical systems perspective: mutual adaptation and incremental learning (2005) — 46 citations
• Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems (1998)