David Edwin Carlson

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 11 papers; trusts 559 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 149 citations into it; #14,648 of 35,345 by trust here, #14,438 by citations
• Computer Vision and Pattern Recognition — 122 citations into it
• Probabilistic & Bayesian Methods — 65 citations into it
• Generative Models — 60 citations into it
• Optimization & Training — 50 citations into it
• Representation & Self-Supervised Learning — 32 citations into it
• Model Architectures — 27 citations into it
• Statistics and Probability — 19 citations into it; #5,388 of 12,932 by trust here, #3,330 by citations

Whom David Edwin Carlson trusts overall

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

1. Radford M. Neal
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Ruslan Salakhutdinov
5. Alex Krizhevsky
6. Yoshua Bengio
7. Ilya Sutskever
8. Yann LeCun
9. Kaiming He
10. Matthew D. Zeiler

Who trusts David Edwin Carlson overall

1. Chunyuan Li
2. Changyou Chen
3. Zhe Gan
4. Ruqi Zhang
5. Stephan Mandt
6. Hao Wang
7. Pratik Chaudhari
8. Lawrence Carin
9. Sai Praneeth Karimireddy

Papers from David Edwin Carlson's perspective

Ordered by how strongly the researchers David Edwin Carlson 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Video Generation From Text (2017) — 317 citations
• Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks (2015) — 368 citations
• StoryGAN: A Sequential Conditional GAN for Story Visualization (2018) — 299 citations
• Deep Temporal Sigmoid Belief Networks for Sequence Modeling (2015) — 91 citations
• Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization (2015) — 94 citations
• The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis (2019) — 40 citations
• Extracting Relationships by Multi-Domain Matching (2018) — 95 citations
• Targeting EEG/LFP Synchrony with Neural Nets (2017) — 67 citations
• Cross-Spectral Factor Analysis (2017) — 26 citations
• Stochastic Spectral Descent for Restricted Boltzmann Machines (2015) — 76 citations
• Learning Deep Sigmoid Belief Networks with Data Augmentation (2015) — 111 citations