Jessy Lin
Artificial Intelligence · 2017–2022
A researcher in the Science corpus: 4 papers; trusts 77 researchers and is trusted by 23 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 116 citations into it; #8,732 of 35,345 by trust here, #8,157 by citations
- Natural Language Processing — 71 citations into it
- Computer Vision and Pattern Recognition — 41 citations into it; #21,169 of 21,575 by trust here, #14,822 by citations
- Safety, Robustness & Interpretability — 34 citations into it; #608 of 2,251 by trust here, #768 by citations
- Reinforcement Learning — 19 citations into it
- Signal Processing — 16 citations into it; #2,395 of 7,572 by trust here, #1,370 by citations
- Optimization & Training — 7 citations into it
- Computational Theory and Mathematics — 6 citations into it
Whom Jessy Lin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Jimmy Ba
- Christian Szegedy
- Yann LeCun
- Kaiming He
- Ian J. Goodfellow
Who trusts Jessy Lin overall
- J'er'emy Scheurer
- Chun Xia
- Nicholas Carlini
- Lukas Schott
- Florian Tramèr
- Jon Ander Campos
- Tianyu Pang
Papers from Jessy Lin's perspective
Ordered by how strongly the researchers Jessy Lin 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
- Gradient-based learning applied to document recognition
- 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)
- 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
- Black-box Adversarial Attacks with Limited Queries and Information (2018) — 1,400 citations
- Query-Efficient Black-box Adversarial Examples (2017) — 53 citations
- Inferring Rewards from Language in Context (2022) — 74 citations
- InCoder: A Generative Model for Code Infilling and Synthesis (2022) — 909 citations