David Snyder

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 9 papers; trusts 48 researchers and is trusted by 93 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #3,303 of 35,345 by trust here, #4,445 by citations
• Signal Processing — 62 citations into it; #403 of 7,572 by trust here, #456 by citations
• Speech & Audio — 52 citations into it; #110 of 1,076 by trust here, #187 by citations
• Computer Vision and Pattern Recognition — 10 citations into it
• Natural Language Processing — 10 citations into it
• Model Architectures — 6 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom David Snyder trusts overall

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

1. Daniel Povey
2. Patrick Kenny
3. Daniel Garcia-Romero
4. Sergey Ioffe
5. Ahmad Salman
6. Sanjeev Khudanpur
7. Yun Lei
8. Vijayaditya Peddinti
9. Najim Dehak
10. Arsha Nagrani

Who trusts David Snyder overall

1. Joon Son Chung
2. Daniel Garcia-Romero
3. Shinji Watanabe
4. Brecht Desplanques
5. Shota Horiguchi
6. Yusuke Fujita
7. Daniel Povey
8. Jenthe Thienpondt
9. Gregory Sell
10. Jaesung Huh

Papers from David Snyder's perspective

Ordered by how strongly the researchers David Snyder 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. Attention is All you Need (Neural Information Processing Systems)
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet classification with deep convolutional neural networks
6. Gradient-based learning applied to document recognition
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• X-Vectors: Robust DNN Embeddings for Speaker Recognition (2018) — 3,060 citations
• Deep Neural Network Embeddings for Text-Independent Speaker Verification (2017) — 796 citations
• Deep neural network-based speaker embeddings for end-to-end speaker verification (2016) — 362 citations
• Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge (2018) — 234 citations
• Speaker Recognition for Multi-speaker Conversations Using X-vectors (2019) — 322 citations
• Spoken Language Recognition using X-vectors (2018) — 246 citations
• Self-Attentive Speaker Embeddings for Text-Independent Speaker Verification (2018) — 269 citations
• MUSAN: A Music, Speech, and Noise Corpus (2015) — 1,702 citations
• Time delay deep neural network-based universal background models for speaker recognition (2015) — 141 citations