Zhong Meng

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 7 papers; trusts 79 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 178 citations into it; #24,305 of 35,345 by trust here, #13,760 by citations
• Signal Processing — 142 citations into it; #5,033 of 7,572 by trust here, #2,457 by citations
• Speech & Audio — 105 citations into it; #944 of 1,076 by trust here, #900 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Representation & Self-Supervised Learning — 24 citations into it; #3,130 of 3,145 by trust here, #2,863 by citations
• Efficiency & Compression — 15 citations into it
• Generative Models — 14 citations into it

Whom Zhong Meng trusts overall

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

1. Jinyu Li
2. Dong Yu
3. Geoffrey E. Hinton
4. Yusuke Shinohara
5. Hasim Sak
6. Yoshua Bengio
7. Hank Liao
8. Yaroslav Ganin
9. Alex Graves
10. Jon Barker

Who trusts Zhong Meng overall

1. Jinyu Li
2. Wei-Ning Hsu
3. Morgane Rivière
4. Yifan Gong
5. Andros Tjandra
6. Yi-Chen Chen
7. Berrak Sisman
8. Hardik B. Sailor
9. Yossi Adi
10. Arun Narayanan

Papers from Zhong Meng's perspective

Ordered by how strongly the researchers Zhong Meng 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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. GENERATIVE ADVERSARIAL NETS

Their papers

• Speaker-Invariant Training Via Adversarial Learning (2018) — 115 citations
• Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages (2023) — 396 citations
• Unsupervised adaptation with domain separation networks for robust speech recognition (2017) — 58 citations
• Continuous Speech Separation: Dataset and Analysis (2020) — 256 citations
• Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone (2021) — 53 citations
• Conditional Teacher-student Learning (2019) — 116 citations
• Cycle-Consistent Speech Enhancement (2018) — 53 citations