Erik McDermott

Artificial Intelligence · 2001–2020

A researcher in the Science corpus: 9 papers; trusts 84 researchers and is trusted by 88 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #2,663 of 35,345 by trust here, #3,687 by citations
• Signal Processing — 66 citations into it; #665 of 7,572 by trust here, #598 by citations
• Natural Language Processing — 42 citations into it; #3,079 of 5,688 by trust here, #5,214 by citations
• Speech & Audio — 38 citations into it; #130 of 1,076 by trust here, #173 by citations
• Model Architectures — 36 citations into it; #1,634 of 2,278 by trust here, #969 by citations
• Optimization & Training — 19 citations into it; #1,083 of 2,601 by trust here, #787 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #16,619 of 21,575 by trust here, #10,154 by citations

Whom Erik McDermott trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Yann LeCun
6. Frederick Jelinek
7. Alex Krizhevsky
8. Jimmy Ba
9. Brian Kingsbury
10. J. Urgen Schmidhuber

Who trusts Erik McDermott overall

1. Li Deng
2. Yajie Miao
3. Tara N. Sainath
4. Matthew Gibson
5. Arun Narayanan
6. Rohit Prabhavalkar
7. John Dines
8. Nelson Morgan
9. Dong Yu
10. Bo Li

Papers from Erik McDermott's perspective

Ordered by how strongly the researchers Erik McDermott trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Sequence discriminative distributed training of long short-term memory recurrent neural networks (2014) — 148 citations
• Large scale deep neural network acoustic modeling with semi-supervised training data for YouTube video transcription (2013) — 216 citations
• Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error (2007) — 638 citations
• Deep neural networks for small footprint text-dependent speaker verification (2014) — 959 citations
• Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss (2020) — 539 citations
• A Density Ratio Approach to Language Model Fusion in End-to-End Automatic Speech Recognition (2019) — 121 citations
• End-to-End Training of Acoustic Models for Large Vocabulary Continuous Speech Recognition with TensorFlow (2017) — 28 citations
• A recognition method with parametric trajectory synthesized using direct relations between static and dynamic feature vector time series (2002) — 14 citations
• An application of discriminative feature extraction to filter-bank-based speech recognition (2001) — 98 citations