Weili Nie

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 7 papers; trusts 605 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 158 citations into it; #16,560 of 21,575 by trust here, #9,305 by citations
• Artificial Intelligence — 147 citations into it; #26,982 of 35,345 by trust here, #13,962 by citations
• Molecular Biology — 141 citations into it
• Generative Models — 88 citations into it
• Materials Chemistry — 61 citations into it
• Computational Theory and Mathematics — 60 citations into it
• Computational Biology & Bioinformatics — 34 citations into it
• Representation & Self-Supervised Learning — 33 citations into it

Whom Weili Nie trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Karen Simonyan
9. Jimmy Ba
10. Christian Szegedy

Who trusts Weili Nie overall

1. Julius Adebayo
2. Michael Sejr Schlichtkrull
3. Nan Liu
4. Mohammadreza Salehi
5. Badhan Chandra Das
6. Alexandros Graikos
7. Nicola De Cao
8. Xiangyu Qi
9. Marco Ancona
10. Michael Muelly

Papers from Weili Nie's perspective

Ordered by how strongly the researchers Weili Nie 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Diffusion Models for Adversarial Purification (2022) — 722 citations
• A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations (2018) — 170 citations
• Controllable and Compositional Generation with Latent-Space Energy-Based Models (2021) — 87 citations
• Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models (2022) — 552 citations
• GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics (2022) — 142 citations
• Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models (2022) — 30 citations
• State-specific protein–ligand complex structure prediction with a multiscale deep generative model (2022) — 173 citations