Chen Zhu
Artificial Intelligence · 2017–2024
A researcher in the Science corpus: 11 papers; trusts 994 researchers and is trusted by 35 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 364 citations into it; #12,488 of 35,345 by trust here, #4,445 by citations
- Computer Vision and Pattern Recognition — 258 citations into it; #5,414 of 21,575 by trust here, #3,909 by citations
- Natural Language Processing — 127 citations into it; #3,488 of 5,688 by trust here, #1,926 by citations
- Safety, Robustness & Interpretability — 79 citations into it; #1,377 of 2,251 by trust here, #939 by citations
- Model Architectures — 64 citations into it; #2,134 of 2,278 by trust here, #2,037 by citations
- Evaluation & Benchmarks — 55 citations into it
- Multimodal Learning — 55 citations into it; #586 of 1,211 by trust here, #520 by citations
- Large Language Models — 46 citations into it
Whom Chen Zhu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Yann LeCun
- Ilya Sutskever
- Ross B. Girshick
- Alex Graves
- Karen Simonyan
Who trusts Chen Zhu overall
- Xiaodong Liu
- Zhe Gan
- Kevin Meng
- Derya Soydaner
- Boxin Wang
- Linjie Li
- Pengcheng He
- Haoming Jiang
- Eric Mitchell
- Tianhao Yang
Papers from Chen Zhu's perspective
Ordered by how strongly the researchers Chen Zhu 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
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Large-Scale Adversarial Training for Vision-and-Language Representation Learning (2020) — 559 citations
- Structured Attentions for Visual Question Answering (2017) — 112 citations
- FreeLB: Enhanced Adversarial Training for Natural Language Understanding (2019) — 520 citations
- Long-Short Transformer: Efficient Transformers for Language and Vision (2021) — 175 citations
- Compressing Neural Networks using the Variational Information Bottleneck (2018) — 196 citations
- Learning From Noisy Anchors for One-Stage Object Detection (2019) — 112 citations
- ODIN: Disentangled Reward Mitigates Hacking in RLHF (2024) — 129 citations
- The Intrinsic Dimension of Images and Its Impact on Learning (2021) — 413 citations
- Modifying Memories in Transformer Models (2020) — 278 citations
- FreeLB: Enhanced Adversarial Training for Language Understanding (2019) — 82 citations
- Transferable Clean-Label Poisoning Attacks on Deep Neural Nets (2019) — 327 citations