Cheung Chi Leung

Artificial Intelligence · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 12 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #13,828 of 35,345 by trust here, #17,110 by citations
• Signal Processing — 30 citations into it; #2,661 of 7,572 by trust here, #2,816 by citations
• Speech & Audio — 26 citations into it; #476 of 1,076 by trust here, #654 by citations
• Representation & Self-Supervised Learning — 19 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it; #1,052 of 1,525 by trust here, #991 by citations

Whom Cheung Chi Leung trusts overall

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

1. Yaodong Zhang
2. Radford M. Neal
3. James R. Glass
4. Thomas Schatz
5. Aren Jansen
6. Herman Kamper
7. Chia-ying Lee
8. Geoffrey E. Hinton
9. Hongjie Chen
10. eon BottouAT

Who trusts Cheung Chi Leung overall

1. Maarten Versteegh
2. Jan Chorowski
3. Siyuan Feng
4. Mingjie Chen
5. Hongjie Chen
6. Sakriani Watiasri Sakti
7. Michael Heck
8. Cory Shain
9. Thomas Schatz
10. Herman Kamper

Papers from Cheung Chi Leung's perspective

Ordered by how strongly the researchers Cheung Chi Leung trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Extracting bottleneck features and word-like pairs from untranscribed speech for feature representation (2017) — 20 citations
• Multilingual bottle-neck feature learning from untranscribed speech (2017) — 31 citations
• Parallel inference of dirichlet process Gaussian mixture models for unsupervised acoustic modeling: a feasibility study (2015) — 76 citations