Richard C. Rose

Artificial Intelligence · 1995–2015

A researcher in the Science corpus: 6 papers; trusts 18 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #31,510 of 35,345 by trust here, #9,275 by citations
• Signal Processing — 28 citations into it; #6,805 of 7,572 by trust here, #1,783 by citations
• Natural Language Processing — 10 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Richard C. Rose trusts overall

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

1. Patrick Kenny
2. Nelson Morgan
3. Geoffrey E. Hinton
4. Hynek Hermansky
5. George E. Dahl
6. David E. Rumelhart
7. Tomi H. Kinnunen
8. Dong Yu
9. Li Deng
10. Abdel-rahman Mohamed

Who trusts Richard C. Rose overall

1. Douglas A. Reynolds
2. Ahmad Salman
3. Nikolaos Mavridis
4. Weicheng Cai
5. Roberto B. Togneri
6. Zhiguang Wang
7. Alireza Farhidzadeh
8. Makarand Tapaswi
9. Yan Michalevsky
10. Weiqiang Zhu

Papers from Richard C. Rose's perspective

Ordered by how strongly the researchers Richard C. Rose 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Learning representations by back-propagating errors

Their papers

• Multilingual acoustic modeling for speech recognition based on subspace Gaussian Mixture Models (2010) — 196 citations
• The subspace Gaussian mixture model - A structured model for speech recognition (2011) — 323 citations
• A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition (2013) — 113 citations
• Deep bottleneck features for i-vector based text-independent speaker verification (2015) — 29 citations
• Automatic speech recognition and speech variability: A review (2007) — 590 citations
• Robust text-independent speaker identification using Gaussian mixture speaker models (1995)