Jun Araki

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 5 papers; trusts 181 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 166 citations into it; #6,535 of 35,345 by trust here, #3,652 by citations
• Natural Language Processing — 128 citations into it
• Computer Vision and Pattern Recognition — 59 citations into it
• Large Language Models — 54 citations into it; #206 of 799 by trust here, #311 by citations
• Information Retrieval & Search — 37 citations into it
• Evaluation & Benchmarks — 32 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Information Systems — 11 citations into it

Whom Jun Araki trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Ilya Sutskever
5. Kaiming He
6. Yann LeCun
7. Alex Krizhevsky
8. Jacob Devlin
9. Alex Graves
10. Jimmy Ba

Who trusts Jun Araki overall

1. Timo Schick
2. Zhengbao Jiang
3. Hinrich Schütze
4. Eyal Ben-David
5. Kaiyang Zhou
6. Guohao Li
7. Pengfei Liu
8. Neeraj Varshney
9. Guanghui Qin
10. Clara Meister

Papers from Jun Araki's perspective

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

Their papers

• How Can We Know What Language Models Know? (2019) — 1,658 citations
• How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering (2020) — 594 citations
• X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models (2020) — 165 citations
• SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains (2023) — 19 citations
• Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer (2022) — 53 citations