Daniel Moyer

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 74 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #30,274 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 40 citations into it; #18,596 of 21,575 by trust here, #12,172 by citations
• Representation & Self-Supervised Learning — 26 citations into it; #3,021 of 3,145 by trust here, #2,404 by citations
• Safety, Robustness & Interpretability — 12 citations into it; #2,134 of 2,251 by trust here, #1,780 by citations
• Generative Models — 10 citations into it
• Optimization & Training — 7 citations into it
• Information Theory — 5 citations into it
• Safety Research — 5 citations into it

Whom Daniel Moyer trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Max Welling
4. Alex Krizhevsky
5. Yoshua Bengio
6. R. Freckleton
7. Alessandro Achille
8. Richard S. Zemel
9. Yann LeCun
10. L. V. D. van der Maaten

Who trusts Daniel Moyer overall

1. Congzheng Song
2. Ninareh Mehrabi
3. Yuzi He
4. Ayush Jaiswal
5. Umang Gupta
6. Greg Ver Steeg
7. Vitaly Shmatikov
8. Fred Morstatter
9. Haohan Wang
10. Keith A. Burghardt

Papers from Daniel Moyer's perspective

Ordered by how strongly the researchers Daniel Moyer 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• Invariant Representations without Adversarial Training (2018) — 250 citations
• Multimodal Representation Learning via Maximization of Local Mutual Information (2021) — 50 citations
• Invariant Representations through Adversarial Forgetting (2019) — 47 citations
• Evading the Adversary in Invariant Representation (2018) — 7 citations