Stephen O. Mussmann

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 5 papers; trusts 243 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 153 citations into it; #13,334 of 35,345 by trust here, #7,945 by citations
• Computer Vision and Pattern Recognition — 121 citations into it
• Natural Language Processing — 50 citations into it; #2,509 of 5,688 by trust here, #3,190 by citations
• Evaluation & Benchmarks — 48 citations into it; #1,311 of 3,147 by trust here, #1,934 by citations
• Representation & Self-Supervised Learning — 25 citations into it
• Optimization & Training — 22 citations into it; #2,043 of 2,601 by trust here, #2,519 by citations
• Model Architectures — 18 citations into it
• Signal Processing — 16 citations into it

Whom Stephen O. Mussmann trusts overall

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

1. Tomas Mikolov
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Tijmen Tieleman
5. Alex Krizhevsky
6. Kaiming He
7. Ilya Sutskever
8. Moses Charikar
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Stephen O. Mussmann overall

1. Hao Hao Tan
2. Mohit Bansal
3. Ruiqi Guo
4. Johnny Tian-Zheng Wei
5. Rishi Bommasani
6. Yang Gao
7. Elizabeth Clark
8. Rowan Zellers
9. Faisal Ladhak
10. Daniel Khashabi

Papers from Stephen O. Mussmann's perspective

Ordered by how strongly the researchers Stephen O. Mussmann 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. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• The price of debiasing automatic metrics in natural language evalaution (2018) — 135 citations
• DataComp: In search of the next generation of multimodal datasets (2023) — 734 citations
• Selection Via Proxy: Efficient Data Selection For Deep Learning (2019) — 497 citations
• Concept Bottleneck Models (2020) — 1,388 citations
• Learning and Inference via Maximum Inner Product Search (2016) — 61 citations