Ryohei Sasano

Artificial Intelligence · 2016–2026

A researcher in the Science corpus: 4 papers; trusts 64 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #21,364 of 35,345 by trust here, #14,718 by citations
• Natural Language Processing — 63 citations into it; #3,643 of 5,688 by trust here, #3,014 by citations
• Computer Vision and Pattern Recognition — 33 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Evaluation & Benchmarks — 18 citations into it
• Model Architectures — 13 citations into it; #1,484 of 2,278 by trust here, #1,352 by citations
• Information Retrieval & Search — 11 citations into it
• Large Language Models — 5 citations into it

Whom Ryohei Sasano trusts overall

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

1. Geoffrey E. Hinton
2. Ilya Sutskever
3. Yoshua Bengio
4. Alex Graves
5. Samuel R. Bowman
6. Diederik P. Kingma
7. Jacob Devlin
8. Sepp Hochreiter
9. Xinyu Crystina Zhang
10. Nils Reimers

Who trusts Ryohei Sasano overall

1. Jun Suzuki
2. Angela Fan
3. Masaaki Nagata

Papers from Ryohei Sasano's perspective

Ordered by how strongly the researchers Ryohei Sasano trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Controlling Output Length in Neural Encoder-Decoders (2016) — 254 citations
• When Is 0.1% Enough? Analyzing the Combined Effects of Dimensionality Reduction and Quantization on Text Embedding Compression (2026)
• Redundancy, Isotropy, and Intrinsic Dimensionality of Prompt-based Text Embeddings (2025)
• Ruri: Japanese General Text Embeddings (2024)