Tomoharu Iwata

Artificial Intelligence · 2010–2018

A researcher in the Science corpus: 11 papers; trusts 572 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 148 citations into it; #31,532 of 35,345 by trust here, #19,018 by citations
• Computer Vision and Pattern Recognition — 96 citations into it; #19,354 of 21,575 by trust here, #15,955 by citations
• Representation & Self-Supervised Learning — 46 citations into it; #2,271 of 3,145 by trust here, #2,587 by citations
• Probabilistic & Bayesian Methods — 45 citations into it
• Natural Language Processing — 35 citations into it; #3,865 of 5,688 by trust here, #4,682 by citations
• Optimization & Training — 29 citations into it
• Generative Models — 28 citations into it; #1,751 of 1,956 by trust here, #1,796 by citations
• Model Architectures — 19 citations into it

Whom Tomoharu Iwata trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Tomas Mikolov
6. Yann LeCun
7. Michael I. Jordan
8. Ilya Sutskever
9. Jimmy Ba
10. Radford M. Neal

Who trusts Tomoharu Iwata overall

1. Matthew J. Johnson
2. David Kristjanson Duvenaud
3. Eleni Sgouritsa
4. Danica J. Sutherland
5. Krikamol Muandet
6. Arash Vahdat
7. Alexander B. Wiltschko
8. Yuya Yoshikawa
9. Paul Micaelli
10. Dominik Janzing

Papers from Tomoharu Iwata's perspective

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

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

Their papers

• Variational Autoencoder with Implicit Optimal Priors (2018) — 70 citations
• Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models (2018) — 28 citations
• Fashion Coordinates Recommender System Using Photographs from Fashion Magazines (2011) — 125 citations
• Zero-shot Domain Adaptation without Domain Semantic Descriptors (2018) — 15 citations
• Non-Linear Regression for Bag-of-Words Data via Gaussian Process Latent Variable Set Model (2015) — 12 citations
• Multiscale recurrent neural network based language model (2015) — 24 citations
• Few-shot learning of neural networks from scratch by pseudo example optimization (2018) — 55 citations
• Imitation networks: Few-shot learning of neural networks from scratch (2018) — 6 citations
• Latent Support Measure Machines for Bag-of-Words Data Classification (2014) — 32 citations
• Warped Mixtures for Nonparametric Cluster Shapes (2012) — 37 citations
• Travel route recommendation using geotags in photo sharing sites (2010) — 342 citations