Anton Ragni

Artificial Intelligence · 2009–2023

A researcher in the Science corpus: 9 papers; trusts 59 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 95 citations into it; #11,770 of 35,345 by trust here, #11,624 by citations
• Signal Processing — 66 citations into it; #2,852 of 7,572 by trust here, #2,001 by citations
• Speech & Audio — 43 citations into it; #537 of 1,076 by trust here, #570 by citations
• Natural Language Processing — 30 citations into it; #3,286 of 5,688 by trust here, #3,897 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Optimization & Training — 15 citations into it
• Model Architectures — 9 citations into it; #1,963 of 2,278 by trust here, #2,162 by citations
• Probabilistic & Bayesian Methods — 8 citations into it

Whom Anton Ragni trusts overall

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

1. Geoffrey E. Hinton
2. Mark John Francis Gales
3. Daniel Povey
4. Navdeep Jaitly
5. Diederik P. Kingma
6. Yoshua Bengio
7. Brian Kingsbury
8. Hynek Hermansky
9. Abdel-rahman Mohamed
10. Jean-Luc Gauvain

Who trusts Anton Ragni overall

1. Andrey Malinin
2. Mark John Francis Gales
3. Adithya Renduchintala
4. Alexis Conneau
5. Gözde Gül Sahin
6. Matthew Wiesner
7. Tom Ko
8. Xiang Yu
9. Xiaodong Cui
10. Yu Zhang

Papers from Anton Ragni's perspective

Ordered by how strongly the researchers Anton Ragni 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. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Data augmentation for low resource languages (2014) — 158 citations
• Low-Resource Speech Recognition and Keyword-Spotting (2017) — 16 citations
• Support vector machines for noise robust ASR (2009) — 33 citations
• Confidence Estimation and Deletion Prediction Using Bidirectional Recurrent Neural Networks (2018) — 33 citations
• Investigation of unsupervised adaptation of DNN acoustic models with filter bank input (2014) — 46 citations
• Multilingual representations for low resource speech recognition and keyword search (2015) — 103 citations
• Incorporating Uncertainty into Deep Learning for Spoken Language Assessment (2017) — 47 citations
• MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training (2023) — 320 citations
• Speech recognition and keyword spotting for low-resource languages: Babel project research at CUED (2014) — 206 citations