Shane Settle

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 4 papers; trusts 147 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #8,169 of 35,345 by trust here, #11,750 by citations
• Signal Processing — 43 citations into it; #2,367 of 7,572 by trust here, #2,911 by citations
• Speech & Audio — 36 citations into it; #334 of 1,076 by trust here, #472 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #9,566 of 21,575 by trust here, #12,419 by citations
• Representation & Self-Supervised Learning — 25 citations into it; #1,531 of 3,145 by trust here, #2,404 by citations
• Natural Language Processing — 17 citations into it
• Multimodal Learning — 14 citations into it
• Optimization & Training — 9 citations into it

Whom Shane Settle trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Herman Kamper
4. Yurii E. Nesterov
5. Yoshua Bengio
6. Sepp Hochreiter
7. Alex Graves
8. Aren Jansen
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Shane Settle overall

1. Herman Kamper
2. Karen Livescu
3. Yu-An Chung
4. David F. Harwath
5. Wanjia He
6. James R. Glass
7. Ankita Pasad
8. Wei-Ning Hsu
9. Grzegorz Chrupała
10. Alexei Baevski

Papers from Shane Settle's perspective

Ordered by how strongly the researchers Shane Settle 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Visually Grounded Learning of Keyword Prediction from Untranscribed Speech (2017) — 64 citations
• Acoustically Grounded Word Embeddings for Improved Acoustics-to-word Speech Recognition (2019) — 35 citations
• Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings (2017) — 91 citations
• Discriminative acoustic word embeddings: Tecurrent neural network-based approaches (2016) — 92 citations