Cody A. Coleman

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 85 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 134 citations into it; #34,885 of 35,345 by trust here, #9,369 by citations
• Computer Vision and Pattern Recognition — 133 citations into it
• Optimization & Training — 59 citations into it
• Model Architectures — 36 citations into it
• Natural Language Processing — 26 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Cody A. Coleman trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ilya Sutskever
7. Ross B. Girshick
8. Yann LeCun
9. X. Zhang
10. Christian Szegedy

Who trusts Cody A. Coleman overall

1. Esteban Real
2. Noveen Sachdeva
3. Rohan Taori
4. Carole-Jean Wu
5. M. Paul
6. Eric Wong
7. D. Narayanan
8. Rohan Anil
9. Josh Cowls
10. Chen Wang

Papers from Cody A. Coleman's perspective

Ordered by how strongly the researchers Cody A. Coleman 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Going deeper with convolutions

Their papers

• Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark (2018) — 126 citations
• Selection Via Proxy: Efficient Data Selection For Deep Learning (2019) — 497 citations
• MLPerf Training Benchmark (2019) — 388 citations
• MLPerf Inference Benchmark (2019) — 720 citations
• MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance (2020) — 194 citations
• DAWNBench : An End-to-End Deep Learning Benchmark and Competition (2017) — 353 citations
• HarvardX and MITx: Two Years of Open Online Courses Fall 2012-Summer 2014 (2015) — 353 citations