Yu Nishiyama

Artificial Intelligence · 2012–2016

A researcher in the Science corpus: 5 papers; trusts 89 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it; #10,969 of 35,345 by trust here, #21,878 by citations
• Probabilistic & Bayesian Methods — 65 citations into it; #692 of 1,525 by trust here, #1,050 by citations
• Statistical Learning Theory — 41 citations into it
• Statistics and Probability — 31 citations into it
• Representation & Self-Supervised Learning — 26 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Aerospace Engineering — 14 citations into it
• Statistical and Nonlinear Physics — 14 citations into it

Whom Yu Nishiyama trusts overall

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

1. Arthur Gretton
2. Le Song
3. Kenji Fukumizu
4. Alex Smola
5. Bernhard Schölkopf
6. Francis R. Bach
7. Bharath K. Sriperumbudur
8. Michael I. Jordan
9. Byron Boots
10. Ingo Steinwart

Who trusts Yu Nishiyama overall

1. Kenji Fukumizu
2. Arthur Gretton
3. Herke Van Hoof
4. Krikamol Muandet
5. Le Song
6. Motonobu Kanagawa
7. Byron Boots
8. Steffen Grünewälder
9. Jan Peters
10. Gerhard Neumann

Papers from Yu Nishiyama's perspective

Ordered by how strongly the researchers Yu Nishiyama 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Hilbert Space Embeddings of POMDPs (2012) — 51 citations
• Filtering with State-Observation Examples via Kernel Monte Carlo Filter (2013) — 22 citations
• Monte Carlo Filtering Using Kernel Embedding of Distributions (2014) — 14 citations
• The Nonparametric Kernel Bayes Smoother (2016) — 12 citations
• Characteristic Kernels and Infinitely Divisible Distributions (2014) — 15 citations