Satoshi Matsuoka
Artificial Intelligence · 2011–2018
A researcher in the Science corpus: 6 papers; trusts 69 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 61 citations into it; #14,979 of 35,345 by trust here, #17,776 by citations
- Optimization & Training — 38 citations into it; #1,893 of 2,601 by trust here, #1,766 by citations
- Computer Vision and Pattern Recognition — 30 citations into it; #18,493 of 21,575 by trust here, #17,757 by citations
- Natural Language Processing — 25 citations into it; #1,974 of 5,688 by trust here, #3,638 by citations
- Representation & Self-Supervised Learning — 19 citations into it; #1,196 of 3,145 by trust here, #2,148 by citations
- Evaluation & Benchmarks — 6 citations into it
- Model Architectures — 6 citations into it; #1,904 of 2,278 by trust here, #2,037 by citations
- Statistical and Nonlinear Physics — 6 citations into it
Whom Satoshi Matsuoka trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Omer Levy
- Tomas Mikolov
- Peter D. Turney
- Marco Baroni
- James Martens
- Yoshua Bengio
- Geoffrey E. Hinton
- Richard Socher
- Yoav Goldberg
- Jeffrey Pennington
Who trusts Satoshi Matsuoka overall
Papers from Satoshi Matsuoka's perspective
Ordered by how strongly the researchers Satoshi Matsuoka trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks (2018) — 102 citations
- Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn’t. (2016) — 275 citations
- Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs (2018) — 39 citations
- Word Embeddings, Analogies, and Machine Learning: Beyond king - man + woman = queen (2016) — 174 citations
- FTI: High performance Fault Tolerance Interface for hybrid systems (2011) — 368 citations
- The International Exascale Software Project roadmap (2011) — 769 citations