Anjuli Kannan

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 12 papers; trusts 107 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 204 citations into it; #4,964 of 35,345 by trust here, #2,070 by citations
• Natural Language Processing — 109 citations into it; #1,479 of 5,688 by trust here, #683 by citations
• Signal Processing — 88 citations into it; #1,294 of 7,572 by trust here, #255 by citations
• Speech & Audio — 85 citations into it; #510 of 1,076 by trust here, #148 by citations
• Model Architectures — 65 citations into it; #1,299 of 2,278 by trust here, #708 by citations
• Computer Vision and Pattern Recognition — 40 citations into it; #9,464 of 21,575 by trust here, #11,950 by citations
• Optimization & Training — 18 citations into it
• Generative Models — 8 citations into it

Whom Anjuli Kannan trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ilya Sutskever
6. Oriol Vinyals
7. Sepp Hochreiter
8. J. Urgen Schmidhuber
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Anjuli Kannan overall

1. Jinyu Li
2. Tara N. Sainath
3. Rohit Prabhavalkar
4. Dong Yu
5. Jian Guan
6. Shinji Watanabe
7. Iulian Serban
8. Bo Li
9. Murali Karthick Baskar
10. Tatsunori B. Hashimoto

Papers from Anjuli Kannan's perspective

Ordered by how strongly the researchers Anjuli Kannan 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• State-of-the-Art Speech Recognition with Sequence-to-Sequence Models (2017) — 1,190 citations
• Smart Reply: Automated Response Suggestion for Email (2016) — 338 citations
• Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling (2019) — 215 citations
• An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model (2017) — 282 citations
• Adversarial Evaluation of Dialogue Models (2017) — 76 citations
• Streaming End-to-end Speech Recognition for Mobile Devices (2018) — 676 citations
• Model Unit Exploration for Sequence-to-Sequence Speech Recognition (2019) — 37 citations
• A Comparison of End-to-End Models for Long-Form Speech Recognition (2019) — 92 citations
• A Streaming On-Device End-To-End Model Surpassing Server-Side Conventional Model Quality and Latency (2020) — 228 citations
• A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition (2018) — 171 citations
• Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model (2019) — 194 citations
• No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models (2017) — 54 citations