Brian Hutchinson

Artificial Intelligence · 2011–2017

A researcher in the Science corpus: 6 papers; trusts 37 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #8,223 of 35,345 by trust here, #13,544 by citations
• Signal Processing — 27 citations into it; #3,216 of 7,572 by trust here, #3,566 by citations
• Natural Language Processing — 19 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it; #9,230 of 21,575 by trust here, #14,478 by citations
• Representation & Self-Supervised Learning — 10 citations into it
• Computational Mathematics — 6 citations into it; #158 of 612 by trust here, #173 by citations
• Speech & Audio — 5 citations into it

Whom Brian Hutchinson trusts overall

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

1. Dong Yu
2. Li Deng
3. Geoffrey E. Hinton
4. Abdel-rahman Mohamed
5. George E. Dahl
6. Yoshua Bengio
7. Andreas Stolcke
8. Jerome R. Bellegarda
9. Tamara G. Kolda
10. Yann LeCun

Who trusts Brian Hutchinson overall

1. Li Deng
2. Dong Yu
3. Xiao-Lei Zhang
4. Xue-wen Chen
5. M. A. Ganaie
6. Po-Sen Huang
7. Zhongyang Han
8. Xiaodong He
9. Xudong Cao
10. Jianshu Chen

Papers from Brian Hutchinson's perspective

Ordered by how strongly the researchers Brian Hutchinson 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• A deep architecture with bilinear modeling of hidden representations: Applications to phonetic recognition (2012) — 48 citations
• Parallel Training for Deep Stacking Networks (2012) — 24 citations
• Tensor Deep Stacking Networks (2013) — 151 citations
• Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams (2017) — 365 citations
• A Sparse Plus Low Rank Maximum Entropy Language Model (2012) — 10 citations
• Low Rank Language Models for Small Training Sets (2011) — 10 citations