Zi-Yi Dou
Artificial Intelligence · 2017–2022
A researcher in the Science corpus: 11 papers; trusts 961 researchers and is trusted by 36 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 408 citations into it; #8,259 of 35,345 by trust here, #5,834 by citations
- Computer Vision and Pattern Recognition — 319 citations into it; #5,164 of 21,575 by trust here, #3,732 by citations
- Natural Language Processing — 240 citations into it; #2,449 of 5,688 by trust here, #2,567 by citations
- Multimodal Learning — 141 citations into it
- Model Architectures — 86 citations into it; #759 of 2,278 by trust here, #604 by citations
- Evaluation & Benchmarks — 75 citations into it; #1,242 of 3,147 by trust here, #1,706 by citations
- Representation & Self-Supervised Learning — 53 citations into it
- Large Language Models — 42 citations into it
Whom Zi-Yi Dou trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Ross B. Girshick
- Yann LeCun
- Jimmy Ba
- Oriol Vinyals
Who trusts Zi-Yi Dou overall
- Yixin Liu
- Timo Schick
- Pengfei Liu
- Wenpeng Yin
- Weizhe Yuan
- Hinrich Schütze
- Graham Neubig
- Panupong Pasupat
- Zhe Gan
- Peng Liu
Papers from Zi-Yi Dou's perspective
Ordered by how strongly the researchers Zi-Yi Dou 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
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- SkipNet: Learning Dynamic Routing in Convolutional Networks (2017) — 763 citations
- An Empirical Study of Training End-to-End Vision-and-Language Transformers (2021) — 462 citations
- Generalized Decoding for Pixel, Image, and Language (2022) — 380 citations
- Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone (2022) — 167 citations
- ExplainaBoard: An Explainable Leaderboard for NLP (2021) — 61 citations
- compare-mt: A Tool for Holistic Comparison of Language Generation Systems (2019) — 118 citations
- GSum: A General Framework for Guided Neural Abstractive Summarization (2020) — 294 citations
- Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks (2019) — 130 citations
- Exploiting Deep Representations for Neural Machine Translation (2018) — 96 citations
- RefSum: Refactoring Neural Summarization (2021) — 43 citations
- Word Alignment by Fine-tuning Embeddings on Parallel Corpora (2021) — 323 citations