Jungo Kasai

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 16 papers; trusts 715 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 696 citations into it; #22,966 of 35,345 by trust here, #6,229 by citations
• Natural Language Processing — 508 citations into it; #2,647 of 5,688 by trust here, #1,735 by citations
• Computer Vision and Pattern Recognition — 286 citations into it
• Evaluation & Benchmarks — 240 citations into it
• Large Language Models — 129 citations into it
• Model Architectures — 63 citations into it
• Representation & Self-Supervised Learning — 55 citations into it; #2,512 of 3,145 by trust here, #2,624 by citations
• Prompting & Reasoning — 43 citations into it

Whom Jungo Kasai trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Christopher D. Manning
7. Kaiming He
8. Yann LeCun
9. Alex Graves
10. Oriol Vinyals

Who trusts Jungo Kasai overall

1. Akari Asai
2. Jiatao Gu
3. Sam Shleifer
4. Shijie Wu
5. Patrick Esser
6. Michihiro Yasunaga
7. Xin Sun
8. Tao Ge
9. Young Jin Kim
10. Heming Xia

Papers from Jungo Kasai's perspective

Ordered by how strongly the researchers Jungo Kasai 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. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Gradient-based learning applied to document recognition

Their papers

• TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering (2023) — 442 citations
• Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation (2020) — 156 citations
• Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand (2021) — 46 citations
• Selective Annotation Makes Language Models Better Few-Shot Learners (2022) — 329 citations
• One Embedder, Any Task: Instruction-Finetuned Text Embeddings (2022) — 475 citations
• RealTime QA: What's the Answer Right Now? (2022) — 301 citations
• One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval (2021) — 83 citations
• Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (2019) — 41 citations
• NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics (2021) — 200 citations
• Robust Multilingual Part-of-Speech Tagging via Adversarial Training (2017) — 109 citations
• BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting (2022) — 114 citations
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
• G ENIE A Leaderboard for Human-in-the-Loop Evaluation of Text Generation (2021)
• Findings of the 2021 Conference on Machine Translation (WMT21) (2021) — 244 citations
• Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation (2020) — 51 citations
• Polyglot Contextual Representations Improve Crosslingual Transfer (2019) — 73 citations