Rie Johnson

Artificial Intelligence · 2011–2017

A researcher in the Science corpus: 8 papers; trusts 349 researchers and is trusted by 539 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it; #1,617 of 35,345 by trust here, #5,565 by citations
• Natural Language Processing — 71 citations into it; #452 of 5,688 by trust here, #1,170 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Model Architectures — 26 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Optimization & Training — 17 citations into it; #77 of 2,601 by trust here, #273 by citations
• Information Systems — 10 citations into it
• Statistics and Probability — 8 citations into it

Whom Rie Johnson trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Tong Zhang
4. Yann LeCun
5. Tomas Mikolov
6. Ilya Sutskever
7. Yoon Kim
8. Alex Krizhevsky
9. Jason Aaron Edward Weston
10. Ronan Collobert

Who trusts Rie Johnson overall

1. Peter Richtárik
2. Zeyuan Allen-Zhu
3. Jakub Konecný
4. Sashank J. Reddi
5. Tong Zhang
6. Nicolas Loizou
7. Francis R. Bach
8. Devendra Singh Sachan
9. Jeremy Howard
10. Jason D. Lee

Papers from Rie Johnson's perspective

Ordered by how strongly the researchers Rie Johnson 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Effective Use of Word Order for Text Categorization with Convolutional Neural Networks (2014) — 910 citations
• Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings (2016) — 263 citations
• Deep Pyramid Convolutional Neural Networks for Text Categorization (2017) — 772 citations
• Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level (2016) — 50 citations
• Semi-Supervised Learning with Multi-View Embedding: Theory and Application with Convolutional Neural Networks (2015) — 6 citations
• Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding (2015) — 354 citations
• Accelerating Stochastic Gradient Descent using Predictive Variance Reduction (2013) — 3,007 citations
• Learning Nonlinear Functions Using Regularized Greedy Forest (2011) — 180 citations