Qian Huang

Artificial Intelligence · ML4H@NeurIPS · 2019–2024

A researcher in the Science corpus: 7 papers; trusts 75 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 192 citations into it
• Computer Vision and Pattern Recognition — 84 citations into it
• Safety, Robustness & Interpretability — 27 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Graph Learning — 19 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Molecular Biology — 12 citations into it

Whom Qian Huang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Diederik P. Kingma
6. Ilya Sutskever
7. Yann LeCun
8. Christian Szegedy
9. Jure Leskovec
10. Ruslan Salakhutdinov

Who trusts Qian Huang overall

1. Naveed Akhtar
2. Weihua Hu
3. Yan Zhang
4. Thomas Liao

Papers from Qian Huang's perspective

Ordered by how strongly the researchers Qian Huang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Holistic Evaluation of Language Models (2023) — 1,863 citations
• Enhancing Adversarial Example Transferability With an Intermediate Level Attack (2019) — 300 citations
• Parsel: A Unified Natural Language Framework for Algorithmic Reasoning (2022) — 17 citations
• Combining Label Propagation and Simple Models Out-performs Graph Neural Networks (2020) — 328 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
• Med-Flamingo: a Multimodal Medical Few-shot Learner (2023) — 613 citations
• Better Set Representations For Relational Reasoning (2020) — 22 citations