Drew A. Hudson
Artificial Intelligence · 2018–2024
A researcher in the Science corpus: 9 papers; trusts 1,427 researchers and is trusted by 2,157 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 230 citations into it; #1,820 of 35,345 by trust here, #1,467 by citations
- Computer Vision and Pattern Recognition — 200 citations into it; #847 of 21,575 by trust here, #1,666 by citations
- Natural Language Processing — 111 citations into it; #684 of 5,688 by trust here, #1,769 by citations
- Multimodal Learning — 56 citations into it; #338 of 1,211 by trust here, #831 by citations
- Evaluation & Benchmarks — 53 citations into it; #215 of 3,147 by trust here, #367 by citations
- Model Architectures — 47 citations into it
- Large Language Models — 29 citations into it
- Representation & Self-Supervised Learning — 28 citations into it
Whom Drew A. Hudson trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Alex Graves
- Diederik P. Kingma
- Ross B. Girshick
- Ilya Sutskever
- Yann LeCun
- L. Eon Bottou
Who trusts Drew A. Hudson overall
- Jaemin Cho
- Zhe Gan
- Pengchuan Zhang
- Mohit Bansal
- Ronghang Hu
- Christopher D. Manning
- Zhengyuan Yang
- Duy-Kien Nguyen
- Jianfeng Wang
- Xiujun Li
Papers from Drew A. Hudson's perspective
Ordered by how strongly the researchers Drew A. Hudson 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
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering (2019) — 3,277 citations
- Compositional Attention Networks for Machine Reasoning (2018) — 624 citations
- Generative Adversarial Transformers (2021) — 214 citations
- GQA: a new dataset for compositional question answering over real-world images (2019) — 155 citations
- Learning by Abstraction: The Neural State Machine (2019) — 286 citations
- Holistic Evaluation of Language Models (2023) — 1,863 citations
- SLM: Learning a Discourse Language Representation with Sentence Unshuffling (2020) — 53 citations
- When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations