適用於公開取用強制性政策的文章 - Michihiro Yasunaga瞭解詳情
在某個資料庫公開的文章：14
Wilds: A benchmark of in-the-wild distribution shifts
PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ...
International conference on machine learning, 5637-5664, 2021
授權規定： US National Science Foundation, US Department of Defense, Marcus and Amalia …
Holistic evaluation of language models
P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ...
arXiv preprint arXiv:2211.09110, 2022
授權規定： US National Science Foundation
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
M Yasunaga, H Ren, A Bosselut, P Liang, J Leskovec
Proceedings of the 2021 Conference of the North American Chapter of the …, 2021
授權規定： US National Science Foundation, US Department of Defense, Chan Zuckerberg …
Deep Bidirectional Language-Knowledge Graph Pretraining
M Yasunaga, A Bosselut, H Ren, X Zhang, CD Manning, P Liang, ...
Advances in Neural Information Processing Systems, 2022
授權規定： US National Science Foundation, US Department of Defense, US National …
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
M Yasunaga, P Liang
Proceedings of the 37th International Conference on Machine Learning, 2020
授權規定： US National Science Foundation
Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL)
MK Chandrasekaran, P Mayr, M Yasunaga, D Freitag, D Radev, MY Kan
Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019
授權規定： German Research Foundation, Chan Zuckerberg Initiative
Extending the wilds benchmark for unsupervised adaptation
S Sagawa, PW Koh, T Lee, I Gao, SM Xie, K Shen, A Kumar, W Hu, ...
International Conference on Learning Representations, 2021
授權規定： US National Science Foundation, US Department of Defense, Marcus and Amalia …
Break-It-Fix-It: Unsupervised Learning for Program Repair
M Yasunaga, P Liang
Proceedings of the 38th International Conference on Machine Learning, 2021
授權規定： US National Science Foundation
Holistic evaluation of text-to-image models
T Lee*, M Yasunaga*, C Meng*, Y Mai, JS Park, A Gupta, Y Zhang, ...
Advances in Neural Information Processing Systems 36, 2024
授權規定： US Department of Defense
Lego: Latent execution-guided reasoning for multi-hop question answering on knowledge graphs
H Ren, H Dai, B Dai, X Chen, M Yasunaga, H Sun, D Schuurmans, ...
International conference on machine learning, 8959-8970, 2021
授權規定： US National Science Foundation, US Department of Defense, US National …
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering
Y Wang, M Yasunaga, H Ren, S Wada, J Leskovec
arXiv preprint arXiv:2205.11501, 2022
授權規定： US National Science Foundation, US Department of Defense, US National …
Med-easi: Finely annotated dataset and models for controllable simplification of medical texts
C Basu, R Vasu, M Yasunaga, Q Yang
Proceedings of the AAAI Conference on Artificial Intelligence, 2023
授權規定： Swiss National Science Foundation
Zero-shot causal learning
H Nilforoshan, M Moor, Y Roohani, Y Chen, A Šurina, M Yasunaga, ...
Advances in Neural Information Processing Systems 36, 6862-6901, 2023
授權規定： US National Science Foundation, US Department of Defense, US National …
Dense Retrieval of Knowledge Graphs for Question Answering
SR Nangi, M Yasunaga, H Ren, Q Huang, P Liang, J Leskovec
授權規定： US Department of Defense
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