Karren D. Yang

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 3 papers; trusts 25 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #14,092 of 35,345 by trust here, #21,878 by citations
• Signal Processing — 32 citations into it
• Causal Inference & Discovery — 22 citations into it; #164 of 364 by trust here, #234 by citations
• Computer Vision and Pattern Recognition — 11 citations into it
• Statistics and Probability — 11 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Molecular Biology — 7 citations into it; #15,429 of 17,773 by trust here, #14,509 by citations
• Representation & Self-Supervised Learning — 6 citations into it

Whom Karren D. Yang trusts overall

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

1. Alain Hauser
2. Tom L. Burr
3. David Maxwell Chickering
4. Kaiming He
5. Jan Peters
6. Geoffrey E. Hinton
7. Peter Buhlmann
8. Diederik P. Kingma
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Karren D. Yang overall

1. Chandler Squires
2. Philippe Brouillard
3. Joris M. Mooij
4. Zhichao Huang
5. Yi-Chiao Wu
6. Yuhao Wang
7. Amin Jaber
8. Caroline Uhler
9. Sébastien Lachapelle
10. Murat Kocaoglu

Papers from Karren D. Yang's perspective

Ordered by how strongly the researchers Karren D. Yang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions (2018) — 135 citations
• Permutation-based Causal Inference Algorithms with Interventions (2017) — 169 citations
• Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis (2022) — 49 citations