Michihiro Yasunaga
Artificial Intelligence · 2017–2025
A researcher in the Science corpus: 27 papers; trusts 936 researchers and is trusted by 57 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 975 citations into it; #4,560 of 35,345 by trust here, #806 by citations
- Natural Language Processing — 531 citations into it; #1,132 of 5,688 by trust here, #746 by citations
- Computer Vision and Pattern Recognition — 386 citations into it
- Evaluation & Benchmarks — 170 citations into it; #3,104 of 3,147 by trust here, #210 by citations
- Large Language Models — 161 citations into it
- Information Systems — 124 citations into it; #1,892 of 8,771 by trust here, #400 by citations
- Representation & Self-Supervised Learning — 103 citations into it
- Prompting & Reasoning — 85 citations into it
Whom Michihiro Yasunaga trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Christopher D. Manning
- Jimmy Ba
- Sepp Hochreiter
- Yann LeCun
Who trusts Michihiro Yasunaga overall
- Percy Liang
- Xinyun Chen
- Daniel Fried
- Theo X. Olausson
- Jure Leskovec
- Tao Yu
- Bowen Jin
- Shirui Pan
- Armen Aghajanyan
- W. Zhang
Papers from Michihiro Yasunaga's perspective
Ordered by how strongly the researchers Michihiro Yasunaga 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
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- 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)
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (2018) — 1,963 citations
- WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) — 1,913 citations
- Holistic Evaluation of Language Models (2023) — 1,863 citations
- REPLUG: Retrieval-Augmented Black-Box Language Models (2023) — 1,051 citations
- Retrieval-Augmented Multimodal Language Modeling (2023) — 166 citations
- UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022) — 362 citations
- QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering (2021) — 863 citations
- CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (2019) — 312 citations
- SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (2018) — 229 citations
- SParC: Cross-Domain Semantic Parsing in Context (2019) — 251 citations
- Break-It-Fix-It: Unsupervised Learning for Program Repair (2021) — 133 citations
- LinkBERT: Pretraining Language Models with Document Links (2022) — 493 citations
- Graph-based Neural Multi-Document Summarization (2017) — 280 citations
- Is ChatGPT a General-Purpose Natural Language Processing Task Solver? (2023) — 872 citations
- Deep Bidirectional Language-Knowledge Graph Pretraining (2022) — 279 citations
- Robust Multilingual Part-of-Speech Tagging via Adversarial Training (2017) — 109 citations
- GreaseLM: Graph REASoning Enhanced Language Models for Question Answering (2022) — 306 citations
- gpt-oss-120b&gpt-oss-20b Model Card (2025) — 1,045 citations
- When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations