Ke Tan

Signal Processing · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 75 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 71 citations into it; #2,617 of 7,572 by trust here, #3,309 by citations
• Artificial Intelligence — 45 citations into it; #23,324 of 35,345 by trust here, #29,412 by citations
• Speech & Audio — 29 citations into it
• Cognitive Neuroscience — 22 citations into it
• Computational Mechanics — 21 citations into it; #4,803 of 10,001 by trust here, #5,567 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Model Architectures — 19 citations into it
• Optimization & Training — 10 citations into it

Whom Ke Tan trusts overall

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

1. Deliang Wang
2. Sergey Ioffe
3. Yuxuan Wang
4. Diederik P. Kingma
5. Sepp Hochreiter
6. Christian Szegedy
7. Xavier Glorot
8. Yoshua Bengio
9. Geoffrey E. Hinton
10. Hakan Erdogan

Who trusts Ke Tan overall

1. Deliang Wang
2. Ashutosh Pandey
3. Zhong-Qiu Wang
4. Xue Jiang
5. Zehua Zhang
6. Kai Zhen
7. Eliya Nachmani
8. Xiulian Peng
9. Chengyu Zheng
10. Lu Zhang

Papers from Ke Tan's perspective

Ordered by how strongly the researchers Ke Tan 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Torchaudio-Squim: Reference-Less Speech Quality and Intelligibility Measures in Torchaudio (2023) — 123 citations
• Learning Complex Spectral Mapping with Gated Convolutional Recurrent Networks for Monaural Speech Enhancement (2019) — 363 citations
• Deep Learning Based Phase Reconstruction for Speaker Separation: A Trigonometric Perspective (2018) — 100 citations
• Gated Residual Networks with Dilated Convolutions for Supervised Speech Separation (2018) — 56 citations
• A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement (2018) — 522 citations