Takashi Morita

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 76 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #13,575 of 35,345 by trust here, #13,962 by citations
• Natural Language Processing — 45 citations into it; #2,540 of 5,688 by trust here, #3,053 by citations
• Cognitive Neuroscience — 23 citations into it
• Developmental and Educational Psychology — 19 citations into it
• Model Architectures — 19 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Speech & Audio — 9 citations into it
• Signal Processing — 8 citations into it

Whom Takashi Morita trusts overall

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

1. Sakriani Watiasri Sakti
2. Ilya Sutskever
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Oriol Vinyals
6. Yoshua Bengio
7. Jeffrey L. Elman
8. M. Masson
9. Yoav Goldberg
10. Alex Graves

Who trusts Takashi Morita overall

1. Alex Warstadt
2. Ethan Gotlieb Wilcox
3. Sebastian Schuster
4. Peng Qian
5. Tal Linzen
6. Richard Futrell

Papers from Takashi Morita's perspective

Ordered by how strongly the researchers Takashi Morita 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• What do RNN Language Models Learn about Filler–Gap Dependencies? (2018) — 206 citations
• RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency (2018) — 64 citations
• Neural language models as psycholinguistic subjects: Representations of syntactic state (2019) — 237 citations
• Exploring TTS without T Using Biologically/Psychologically Motivated Neural Network Modules (ZeroSpeech 2020) (2020) — 8 citations