Hao Cheng

Artificial Intelligence · with Gao · 2013–2023

A researcher in the Science corpus: 15 papers; trusts 550 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 420 citations into it; #14,861 of 35,345 by trust here, #4,316 by citations
• Natural Language Processing — 279 citations into it; #5,071 of 5,688 by trust here, #3,053 by citations
• Computer Vision and Pattern Recognition — 221 citations into it; #16,745 of 21,575 by trust here, #6,671 by citations
• Large Language Models — 111 citations into it
• Evaluation & Benchmarks — 97 citations into it
• Representation & Self-Supervised Learning — 42 citations into it
• Model Architectures — 31 citations into it
• Information Retrieval & Search — 26 citations into it

Whom Hao Cheng trusts overall

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

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

Who trusts Hao Cheng overall

1. Barbara Plank
2. Héctor Martínez Alonso
3. Martin Fajcik
4. Sebastian Ruder
5. Martin Docekal
6. Xiaodong Liu
7. Michihiro Yasunaga
8. Marek Rei
9. Nasrin Mostafazadeh
10. Zhe Gan

Papers from Hao Cheng's perspective

Ordered by how strongly the researchers Hao Cheng 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• Language Models for Image Captioning: The Quirks and What Works (2015) — 293 citations
• Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing (2020) — 2,598 citations
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback (2023) — 530 citations
• Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models (2023) — 483 citations
• Sounding Board: A User-Centric and Content-Driven Social Chatbot (2018) — 61 citations
• The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding (2020) — 62 citations
• Dialogue State Tracking with a Language Model using Schema-Driven Prompting (2021) — 148 citations
• Open-Domain Name Error Detection using a Multi-Task RNN (2015) — 48 citations
• Adversarial Training for Large Neural Language Models (2020) — 206 citations
• UnitedQA: A Hybrid Approach for Open Domain Question Answering (2021) — 65 citations
• Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering (2020) — 22 citations
• MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts (2023) — 1,741 citations
• Visually-Augmented Language Modeling (2022) — 22 citations
• Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention (2021) — 64 citations
• Convex Two-Layer Modeling (2013) — 12 citations