Jiacheng Liu
Artificial Intelligence · 2021–2023
A researcher in the Science corpus: 7 papers; trusts 90 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 233 citations into it; #34,065 of 35,345 by trust here, #13,163 by citations
- Natural Language Processing — 107 citations into it; #4,892 of 5,688 by trust here, #2,422 by citations
- Large Language Models — 88 citations into it
- Computer Vision and Pattern Recognition — 87 citations into it
- Prompting & Reasoning — 58 citations into it; #276 of 359 by trust here, #218 by citations
- Information Systems — 25 citations into it; #8,666 of 8,771 by trust here, #5,078 by citations
- Computational Theory and Mathematics — 13 citations into it
- Program Analysis & Verification — 8 citations into it
Whom Jiacheng Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Ilya Sutskever
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Kaiming He
- Yann LeCun
- Christian Szegedy
- Richard Socher
- Alec Radford
Who trusts Jiacheng Liu overall
- Sean Welleck
- Zhangir Azerbayev
- Hangfeng He
- Simon Frieder
- Albert Qiaochu Jiang
- Ofir Press
- Jaehun Jung
Papers from Jiacheng Liu's perspective
Ordered by how strongly the researchers Jiacheng Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- 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)
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- NaturalProofs: Mathematical Theorem Proving in Natural Language (2021) — 107 citations
- Inverse Scaling: When Bigger Isn't Better (2023) — 235 citations
- Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs (2022) — 338 citations
- NaturalProver: Grounded Mathematical Proof Generation with Language Models (2022) — 105 citations
- MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts (2023) — 1,741 citations
- Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering (2022) — 64 citations
- Generated Knowledge Prompting for Commonsense Reasoning (2021) — 407 citations