Pegah Ghahremani

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 7 papers; trusts 40 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #5,922 of 35,345 by trust here, #4,829 by citations
• Signal Processing — 61 citations into it; #1,256 of 7,572 by trust here, #575 by citations
• Speech & Audio — 49 citations into it; #388 of 1,076 by trust here, #293 by citations
• Natural Language Processing — 25 citations into it; #5,564 of 5,688 by trust here, #4,112 by citations
• Model Architectures — 16 citations into it; #1,942 of 2,278 by trust here, #1,976 by citations
• Representation & Self-Supervised Learning — 11 citations into it; #2,569 of 3,145 by trust here, #2,757 by citations
• Efficiency & Compression — 5 citations into it

Whom Pegah Ghahremani trusts overall

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

1. Daniel Povey
2. Geoffrey E. Hinton
3. Vijayaditya Peddinti
4. Arturo Camacho
5. Yoshua Bengio
6. Hynek Hermansky
7. R. Caruana
8. Dimitri Palaz
9. Alex Graves
10. Florin Curelaru

Who trusts Pegah Ghahremani overall

1. Vimal Manohar
2. Takaaki Hori
3. Daniel Povey
4. Mirco Ravanelli
5. Shinji Watanabe
6. Sanjeev Khudanpur
7. Arun Narayanan
8. David Snyder
9. Yi Luo
10. Andros Tjandra

Papers from Pegah Ghahremani's perspective

Ordered by how strongly the researchers Pegah Ghahremani 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. 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. 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

• Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI (2016) — 891 citations
• A Time-Restricted Self-Attention Layer for ASR (2018) — 163 citations
• Investigation of transfer learning for ASR using LF-MMI trained neural networks (2017) — 75 citations
• A pitch extraction algorithm tuned for automatic speech recognition (2014) — 350 citations
• Acoustic Modelling from the Signal Domain Using CNNs (2016) — 92 citations
• Deep neural network-based speaker embeddings for end-to-end speaker verification (2016) — 362 citations
• An empirical evaluation of zero resource acoustic unit discovery (2017) — 14 citations