Pengcheng Yin

Artificial Intelligence · 2014–2024

A researcher in the Science corpus: 16 papers; trusts 592 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 566 citations into it; #3,996 of 35,345 by trust here, #862 by citations
• Natural Language Processing — 365 citations into it; #1,110 of 5,688 by trust here, #1,066 by citations
• Computer Vision and Pattern Recognition — 200 citations into it; #6,049 of 21,575 by trust here, #970 by citations
• Information Systems — 95 citations into it; #2,913 of 8,771 by trust here, #885 by citations
• Evaluation & Benchmarks — 75 citations into it
• Model Architectures — 65 citations into it
• Safety, Robustness & Interpretability — 59 citations into it
• Prompting & Reasoning — 38 citations into it

Whom Pengcheng Yin trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Oriol Vinyals
7. Alex Graves
8. Yann LeCun
9. Percy Liang
10. Kaiming He

Who trusts Pengcheng Yin overall

1. Zhuang Li
2. Frank F. Xu
3. Zhiruo Wang
4. Graham Neubig
5. Mikhail Evtikhiev
6. Lizhen Qu
7. Pietro Liguori
8. Wenhu Chen
9. Gerhard Weikum
10. Vadim Borisov

Papers from Pengcheng Yin's perspective

Ordered by how strongly the researchers Pengcheng Yin 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. 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. 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

• PaLM: Scaling Language Modeling with Pathways (2022) — 8,214 citations
• DyNet: The Dynamic Neural Network Toolkit (2017) — 391 citations
• UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022) — 362 citations
• Natural Language to Code Generation in Interactive Data Science Notebooks (2022) — 122 citations
• Retrieval-Based Neural Code Generation (2018) — 122 citations
• Neural Enquirer: Learning to Query Tables (2015) — 41 citations
• High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks (2019) — 708 citations
• StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing (2018) — 103 citations
• Learning to Represent Edits (2018) — 120 citations
• TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data (2020) — 818 citations
• Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow (2018) — 366 citations
• TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation (2018) — 252 citations
• A Tree-based Decoder for Neural Machine Translation (2018) — 57 citations
• Incorporating External Knowledge through Pre-training for Natural Language to Code Generation (2020) — 88 citations
• Multiplexed 3D Cellular Super-Resolution Imaging with DNA-PAINT and Exchange-PAINT (2014) — 1,007 citations
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (2024) — 96 citations