Shigeki Karita
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 5 papers; trusts 62 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 133 citations into it; #17,923 of 35,345 by trust here, #5,424 by citations
- Natural Language Processing — 64 citations into it
- Signal Processing — 58 citations into it; #5,231 of 7,572 by trust here, #1,136 by citations
- Speech & Audio — 55 citations into it; #676 of 1,076 by trust here, #231 by citations
- Computer Vision and Pattern Recognition — 34 citations into it
- Model Architectures — 28 citations into it
- Generative Models — 9 citations into it
- Optimization & Training — 8 citations into it
Whom Shigeki Karita trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Graves
- Yoshua Bengio
- Dzmitry Bahdanau
- Jimmy Ba
- Yann LeCun
- Kaiming He
- Alex Krizhevsky
- Ilya Sutskever
Who trusts Shigeki Karita overall
- Tomoki Hayashi
- Shinji Watanabe
- Jinyu Li
- Takaaki Hori
- Dongwei Jiang
- Katsuki Inoue
- Wenxin Hou
- Cong Han
- Pengcheng Guo
- Haoneng Luo
Papers from Shigeki Karita's perspective
Ordered by how strongly the researchers Shigeki Karita 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
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- ESPnet: End-to-End Speech Processing Toolkit (2018) — 1,730 citations
- A Comparative Study on Transformer vs RNN in Speech Applications (2019) — 796 citations
- Improving Transformer-Based End-to-End Speech Recognition with Connectionist Temporal Classification and Language Model Integration (2019) — 231 citations
- Unsupervised Learning of Disentangled Speech Content and Style Representation (2020) — 21 citations
- ESPnet-ST: All-in-One Speech Translation Toolkit (2020) — 174 citations