Baobao Chang

Artificial Intelligence · 2014–2023

A researcher in the Science corpus: 14 papers; trusts 483 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 396 citations into it; #4,301 of 35,345 by trust here, #3,128 by citations
• Natural Language Processing — 216 citations into it; #1,354 of 5,688 by trust here, #884 by citations
• Computer Vision and Pattern Recognition — 175 citations into it
• Prompting & Reasoning — 72 citations into it
• Evaluation & Benchmarks — 48 citations into it
• Model Architectures — 25 citations into it
• Optimization & Training — 24 citations into it
• Representation & Self-Supervised Learning — 21 citations into it

Whom Baobao Chang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Richard Socher
5. Ilya Sutskever
6. Alex Krizhevsky
7. Christopher D. Manning
8. Sepp Hochreiter
9. Yann LeCun
10. Alex Graves

Who trusts Baobao Chang overall

1. Yoav Goldberg
2. Zhuosheng Zhang
3. Masatoshi Tsuchiya
4. Yonatan Belinkov
5. Minghao Hu
6. Yi Tay
7. Hsin-Yuan Huang
8. Eliyahu Kiperwasser
9. Yichen Gong
10. Jiwei Li

Papers from Baobao Chang's perspective

Ordered by how strongly the researchers Baobao Chang 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. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• Gated Self-Matching Networks for Reading Comprehension and Question Answering (2017) — 673 citations
• Table-to-text Generation by Structure-aware Seq2seq Learning (2017) — 284 citations
• Order-Planning Neural Text Generation From Structured Data (2017) — 117 citations
• A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer (2019) — 188 citations
• An Effective Neural Network Model for Graph-based Dependency Parsing (2015) — 62 citations
• Max-Margin Tensor Neural Network for Chinese Word Segmentation (2014) — 168 citations
• A Survey on In-context Learning (2022) — 1,101 citations
• Learning to Control the Fine-grained Sentiment for Story Ending Generation (2019) — 54 citations
• MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning (2023) — 213 citations
• A Survey for In-context Learning (2023) — 401 citations
• Can We Edit Factual Knowledge by In-Context Learning? (2023) — 356 citations
• Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View (2021) — 37 citations
• Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning (2021) — 228 citations
• Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition (2016) — 68 citations