Mike Wu

Artificial Intelligence · with Goodman · 2017–2022

A researcher in the Science corpus: 7 papers; trusts 500 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 166 citations into it; #20,211 of 35,345 by trust here, #11,490 by citations
• Computer Vision and Pattern Recognition — 117 citations into it; #12,682 of 21,575 by trust here, #14,822 by citations
• Probabilistic & Bayesian Methods — 75 citations into it
• Representation & Self-Supervised Learning — 54 citations into it; #2,059 of 3,145 by trust here, #2,624 by citations
• Generative Models — 45 citations into it
• Model Architectures — 26 citations into it
• Statistics and Probability — 24 citations into it
• Signal Processing — 19 citations into it

Whom Mike Wu trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Kaiming He
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Radford M. Neal
9. Michael I. Jordan
10. Ross B. Girshick

Who trusts Mike Wu overall

1. Isaac Lage
2. Yuge Shi
3. Noah D. Goodman
4. Nadia Burkart
5. Stephan Alaniz
6. Guang Yang
7. D. V. Carvalho
8. Sana Tonekaboni
9. Zhibo Zhang
10. Gagan Bansal

Papers from Mike Wu's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Multimodal Generative Models for Scalable Weakly-Supervised Learning (2018) — 499 citations
• Meta-Amortized Variational Inference and Learning (2019) — 40 citations
• Variational Item Response Theory: Fast, Accurate, and Expressive (2020) — 62 citations
• Foundation Posteriors for Approximate Probabilistic Inference (2022) — 9 citations
• Modeling Item Response Theory with Stochastic Variational Inference (2021) — 5 citations
• Conditional Negative Sampling for Contrastive Learning of Visual Representations (2020) — 91 citations
• Beyond Sparsity: Tree Regularization of Deep Models for Interpretability (2017) — 300 citations