Freda Shi
Artificial Intelligence · 2018–2023
A researcher in the Science corpus: 8 papers; trusts 408 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 368 citations into it; #18,016 of 35,345 by trust here, #5,565 by citations
- Natural Language Processing — 228 citations into it; #3,529 of 5,688 by trust here, #1,558 by citations
- Computer Vision and Pattern Recognition — 132 citations into it; #15,009 of 21,575 by trust here, #14,151 by citations
- Large Language Models — 100 citations into it
- Prompting & Reasoning — 54 citations into it
- Evaluation & Benchmarks — 46 citations into it
- Multimodal Learning — 32 citations into it
- Software — 27 citations into it; #1,197 of 1,227 by trust here, #85 by citations
Whom Freda Shi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Yann LeCun
- Jimmy Ba
- Richard Socher
- Ruslan Salakhutdinov
Who trusts Freda Shi overall
- Xinyun Chen
- Yanpeng Zhao
- Lifeng Jin
- Jiayuan Mao
- Ansong Ni
- W. Zhang
- Taeuk Kim
- Nathanael Scharli
- Kechi Zhang
- Fengji Zhang
Papers from Freda Shi's perspective
Ordered by how strongly the researchers Freda Shi trusts endorse each paper. Same corpus, a different order for every researcher.
- 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Language Models are Multilingual Chain-of-Thought Reasoners (2022) — 634 citations
- Visually Grounded Neural Syntax Acquisition (2019) — 87 citations
- Natural Language to Code Translation with Execution (2022) — 181 citations
- Learning Visually-Grounded Semantics from Contrastive Adversarial Samples (2018) — 53 citations
- Substructure Substitution: Structured Data Augmentation for NLP (2021) — 54 citations
- Large Language Models Can Be Easily Distracted by Irrelevant Context (2023) — 1,096 citations
- Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models (2023) — 1,079 citations
- InCoder: A Generative Model for Code Infilling and Synthesis (2022) — 909 citations