Hao Tang

Artificial Intelligence · with Glass · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 214 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it; #5,443 of 35,345 by trust here, #7,657 by citations
• Signal Processing — 70 citations into it; #811 of 7,572 by trust here, #861 by citations
• Speech & Audio — 49 citations into it; #356 of 1,076 by trust here, #400 by citations
• Computer Vision and Pattern Recognition — 45 citations into it; #18,942 of 21,575 by trust here, #17,258 by citations
• Natural Language Processing — 35 citations into it
• Representation & Self-Supervised Learning — 31 citations into it; #1,088 of 3,145 by trust here, #1,301 by citations
• Model Architectures — 30 citations into it
• Optimization & Training — 15 citations into it

Whom Hao Tang trusts overall

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

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

Who trusts Hao Tang overall

1. Yu-An Chung
2. James R. Glass
3. Shaoshi Ling
4. Andy T. Liu
5. Wei-Ning Hsu
6. Puyuan Peng
7. David F. Harwath
8. Alexander H. Liu
9. Grzegorz Chrupała
10. Lasse Borgholt

Papers from Hao Tang's perspective

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

Their papers

• A Deep Residual Network for Large-Scale Acoustic Scene Analysis (2019) — 52 citations
• An Unsupervised Autoregressive Model for Speech Representation Learning (2019) — 432 citations
• Vector-Quantized Autoregressive Predictive Coding (2020) — 129 citations
• On Training Recurrent Networks with Truncated Backpropagation Through time in Speech Recognition (2018) — 23 citations
• A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition (2018) — 26 citations
• Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition (2017) — 124 citations