Zhiyong Wu

Artificial Intelligence · Conference on Empirical Methods in Natural Language Processing · 2020–2024

A researcher in the Science corpus: 7 papers; trusts 256 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 281 citations into it; #33,308 of 35,345 by trust here, #12,387 by citations
• Computer Vision and Pattern Recognition — 132 citations into it
• Natural Language Processing — 131 citations into it
• Evaluation & Benchmarks — 38 citations into it
• Multimodal Learning — 35 citations into it
• Generative Models — 22 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Zhiyong Wu trusts overall

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

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

Who trusts Zhiyong Wu overall

1. Yonatan Belinkov
2. Koustuv Sinha
3. Prasanna Parthasarathi
4. Zhuosheng Zhang

Papers from Zhiyong Wu's perspective

Ordered by how strongly the researchers Zhiyong Wu 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. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT (2020) — 216 citations
• ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022) — 305 citations
• DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models (2022) — 592 citations
• A Survey on In-context Learning (2022) — 1,101 citations
• SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents (2024) — 561 citations
• Can We Edit Factual Knowledge by In-Context Learning? (2023) — 356 citations
• Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation (2021) — 106 citations