Nina M. Moorman

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it
• Computer Vision and Pattern Recognition — 28 citations into it
• Reinforcement Learning — 23 citations into it

Whom Nina M. Moorman trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Pieter Abbeel
6. Sergey Levine
7. L. V. D. van der Maaten
8. Yann LeCun
9. Richard S. Sutton
10. Ilya Sutskever

Papers from Nina M. Moorman's perspective

Ordered by how strongly the researchers Nina M. Moorman 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• LanCon-Learn: Learning with Language to Enable Generalization in Multi-Task Manipulation (2021) — 40 citations