Hai Zhao

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 12 papers; trusts 639 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 419 citations into it; #33,347 of 35,345 by trust here, #7,172 by citations
• Natural Language Processing — 284 citations into it
• Computer Vision and Pattern Recognition — 219 citations into it; #20,433 of 21,575 by trust here, #8,122 by citations
• Large Language Models — 103 citations into it
• Evaluation & Benchmarks — 91 citations into it
• Multimodal Learning — 63 citations into it
• Model Architectures — 57 citations into it
• Representation & Self-Supervised Learning — 23 citations into it

Whom Hai Zhao trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Alex Krizhevsky
6. Kyunghyun Cho
7. Kaiming He
8. Dzmitry Bahdanau
9. Jimmy Ba
10. Yann LeCun

Who trusts Hai Zhao overall

1. Felix Stahlberg

Papers from Hai Zhao's perspective

Ordered by how strongly the researchers Hai Zhao trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Multimodal Chain-of-Thought Reasoning in Language Models (2023) — 914 citations
• Retrospective Reader for Machine Reading Comprehension (2020) — 241 citations
• SG-Net: Syntax-Guided Machine Reading Comprehension (2019) — 195 citations
• Syntax-Aware Data Augmentation for Neural Machine Translation (2020) — 23 citations
• Universal Multimodal Representation for Language Understanding (2023) — 35 citations
• CMMLU: Measuring massive multitask language understanding in Chinese (2023) — 512 citations
• Baichuan 2: Open Large-scale Language Models (2023) — 1,014 citations
• Task-specific Objectives of Pre-trained Language Models for Dialogue Adaptation (2020) — 23 citations
• Data-dependent Gaussian Prior Objective for Language Generation (2020) — 69 citations
• Neural Machine Translation with Universal Visual Representation (2020) — 122 citations
• Effective Character-Augmented Word Embedding for Machine Reading Comprehension (2018) — 17 citations
• Finding Better Subword Segmentation for Neural Machine Translation (2018) — 24 citations