Brendan Dolan-Gavitt
Artificial Intelligence · 2017–2023
A researcher in the Science corpus: 8 papers; trusts 241 researchers and is trusted by 118 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 120 citations into it; #2,271 of 35,345 by trust here, #3,638 by citations
- Information Systems — 69 citations into it
- Computer Vision and Pattern Recognition — 51 citations into it
- Natural Language Processing — 48 citations into it
- Signal Processing — 47 citations into it; #237 of 7,572 by trust here, #304 by citations
- Safety, Robustness & Interpretability — 45 citations into it; #142 of 2,251 by trust here, #278 by citations
- Software — 39 citations into it
- Large Language Models — 26 citations into it
Whom Brendan Dolan-Gavitt trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yann LeCun
- Ilya Sutskever
- Yoshua Bengio
- Kaiming He
- Shaoqing Ren
- Christian Szegedy
- Alex Graves
- Diederik P. Kingma
Who trusts Brendan Dolan-Gavitt overall
- Eugene Bagdasarian
- Alexander Turner
- Nicholas Carlini
- Yige Li
- Clement Fung
- Yuanshun Yao
- Dongxian Wu
- Dimitris Tsipras
- Fanchao Qi
- Brandon Tran
Papers from Brendan Dolan-Gavitt's perspective
Ordered by how strongly the researchers Brendan Dolan-Gavitt trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain (2017) — 2,322 citations
- Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions (2021) — 856 citations
- Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks (2018) — 1,338 citations
- BadNets: Evaluating Backdooring Attacks on Deep Neural Networks (2019) — 1,380 citations
- VeriGen: A Large Language Model for Verilog Code Generation (2023) — 389 citations
- Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants (2022) — 184 citations
- An Empirical Cybersecurity Evaluation of GitHub Copilot's Code Contributions (2021) — 51 citations
- Examining Zero-Shot Vulnerability Repair with Large Language Models (2021) — 339 citations