Shigeki Matsuda
Artificial Intelligence · 2012–2016
A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 19 citations into it; #14,455 of 35,345 by trust here, #12,387 by citations
- Natural Language Processing — 13 citations into it
- Signal Processing — 8 citations into it; #2,762 of 7,572 by trust here, #1,857 by citations
- Computer Vision and Pattern Recognition — 5 citations into it
- Large Language Models — 5 citations into it
- Representation & Self-Supervised Learning — 5 citations into it
Whom Shigeki Matsuda trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Holger Schwenk
- Ming Yuan
- Pascal Vincent
- Yann LeCun
- Alex Krizhevsky
- Tomas Mikolov
- Daniel Povey
- Israel Cohen
Who trusts Shigeki Matsuda overall
Papers from Shigeki Matsuda's perspective
Ordered by how strongly the researchers Shigeki Matsuda 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Speech enhancement based on deep denoising autoencoder (2013) — 851 citations
- Automatic node selection for Deep Neural Networks using Group Lasso regularization (2016) — 25 citations
- Factored recurrent neural network language model in TED lecture transcription (2012) — 12 citations