Samuel Thomas

Artificial Intelligence · 2010–2021

A researcher in the Science corpus: 13 papers; trusts 285 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 149 citations into it; #5,601 of 35,345 by trust here, #4,420 by citations
• Computer Vision and Pattern Recognition — 148 citations into it
• Signal Processing — 103 citations into it; #1,083 of 7,572 by trust here, #741 by citations
• Multimodal Learning — 68 citations into it
• Representation & Self-Supervised Learning — 64 citations into it; #2,800 of 3,145 by trust here, #2,120 by citations
• Speech & Audio — 53 citations into it; #286 of 1,076 by trust here, #205 by citations
• Natural Language Processing — 28 citations into it
• Optimization & Training — 13 citations into it

Whom Samuel Thomas trusts overall

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

1. Hynek Hermansky
2. Geoffrey E. Hinton
3. Dong Yu
4. Yoshua Bengio
5. Diederik P. Kingma
6. Kaiming He
7. Alex Krizhevsky
8. Yann LeCun
9. Ilya Sutskever
10. George E. Dahl

Who trusts Samuel Thomas overall

1. Pawel Swietojanski
2. Herman Kamper
3. Arnab Ghoshal
4. Karel Veselý
5. Sree Hari Krishnan Parthasarathi
6. S. Renals
7. Peter Bell
8. Zolt'an Tuske
9. Sibo Tong
10. Thomas Schatz

Papers from Samuel Thomas's perspective

Ordered by how strongly the researchers Samuel Thomas 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. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• AVLnet: Learning Audio-Visual Language Representations from Instructional Videos (2020) — 148 citations
• Multilingual acoustic modeling for speech recognition based on subspace Gaussian Mixture Models (2010) — 196 citations
• Deep neural network features and semi-supervised training for low resource speech recognition (2013) — 154 citations
• English Conversational Telephone Speech Recognition by Humans and Machines (2017) — 374 citations
• Multilingual MLP features for low-resource LVCSR systems (2012) — 122 citations
• The subspace Gaussian mixture model - A structured model for speech recognition (2011) — 323 citations
• Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos (2021) — 100 citations
• A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition (2013) — 113 citations
• Understanding Unequal Gender Classification Accuracy from Face Images (2018) — 53 citations
• Weak top-down constraints for unsupervised acoustic model training (2013) — 61 citations
• Grounding Spoken Words in Unlabeled Video (2019) — 24 citations
• Efficient Knowledge Distillation from an Ensemble of Teachers (2017) — 280 citations
• Invariant Representations for Noisy Speech Recognition (2016) — 70 citations