Yusuke Iwasawa

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 364 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 165 citations into it; #8,117 of 35,345 by trust here, #2,869 by citations
• Computer Vision and Pattern Recognition — 113 citations into it
• Natural Language Processing — 37 citations into it; #2,418 of 5,688 by trust here, #3,154 by citations
• Representation & Self-Supervised Learning — 29 citations into it
• Model Architectures — 19 citations into it
• Prompting & Reasoning — 17 citations into it; #116 of 359 by trust here, #93 by citations
• Evaluation & Benchmarks — 15 citations into it
• Generative Models — 10 citations into it; #796 of 1,956 by trust here, #1,150 by citations

Whom Yusuke Iwasawa trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Jimmy Ba
7. Yann LeCun
8. Ilya Sutskever
9. Ross B. Girshick
10. Karen Simonyan

Who trusts Yusuke Iwasawa overall

1. Chenpeng Du
2. Marvin Zhang
3. Qin Wang
4. Wei-Ning Hsu
5. Sergey Levine
6. K. Yu
7. Olga Fink
8. Gustav Eje Henter
9. Noé Tits
10. Ghodai M. Abdelrahman

Papers from Yusuke Iwasawa's perspective

Ordered by how strongly the researchers Yusuke Iwasawa 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Large Language Models are Zero-Shot Reasoners (2022) — 7,591 citations
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023) — 1,105 citations
• Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder (2018) — 145 citations
• Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (2021) — 424 citations
• Graph-based knowledge tracing: Modeling student proficiency using graph neural networks (2019) — 476 citations