Jason Li

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 7 papers; trusts 59 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 112 citations into it; #12,666 of 35,345 by trust here, #8,210 by citations
• Natural Language Processing — 63 citations into it; #4,516 of 5,688 by trust here, #3,332 by citations
• Computer Vision and Pattern Recognition — 49 citations into it
• Signal Processing — 42 citations into it; #4,349 of 7,572 by trust here, #1,485 by citations
• Optimization & Training — 40 citations into it
• Speech & Audio — 34 citations into it; #830 of 1,076 by trust here, #552 by citations
• Model Architectures — 29 citations into it; #2,197 of 2,278 by trust here, #1,976 by citations
• Evaluation & Benchmarks — 9 citations into it

Whom Jason Li trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Graves
4. Yoshua Bengio
5. Ronan Collobert
6. Jimmy Ba
7. Alex Krizhevsky
8. Ilya Loshchilov
9. Ilya Sutskever
10. Yann LeCun

Who trusts Jason Li overall

1. Adam Polyak
2. Boris Ginsburg
3. Stanislav Beliaev
4. Yu Zhang
5. Albert Zeyer
6. Lior Wolf
7. Nick Rossenbach
8. Samuel Kriman
9. Evelina Bakhturina
10. Vitaly Lavrukhin

Papers from Jason Li's perspective

Ordered by how strongly the researchers Jason Li 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Jasper: An End-to-End Convolutional Neural Acoustic Model (2019) — 288 citations
• NeMo: a toolkit for building AI applications using Neural Modules (2019) — 411 citations
• Quartznet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions (2019) — 328 citations
• Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq (2018) — 52 citations
• Training Neural Speech Recognition Systems with Synthetic Speech Augmentation (2018) — 61 citations
• Mellotron: Multispeaker Expressive Voice Synthesis by Conditioning on Rhythm, Pitch and Global Style Tokens (2019) — 165 citations
• Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks (2019) — 108 citations