Zi-Hua Zhang

Artificial Intelligence · 2022–2023

A researcher in the Science corpus: 6 papers; trusts 45 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 213 citations into it
• Speech & Audio — 150 citations into it
• Signal Processing — 109 citations into it
• Natural Language Processing — 53 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Model Architectures — 20 citations into it
• Evaluation & Benchmarks — 17 citations into it

Whom Zi-Hua Zhang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Graves
4. Yoshua Bengio
5. Ashish Vaswani
6. Ilya Sutskever
7. Alexei Baevski
8. Alex Krizhevsky
9. Jimmy Ba
10. Wei-Ning Hsu

Who trusts Zi-Hua Zhang overall

1. Dong Zhang
2. Long Zhou
3. Krishna C. Puvvada
4. Sanyuan Chen
5. Ziyue Jiang
6. Shujie Liu
7. Mateusz Lajszczak
8. Qianqian Dong
9. Dongchao Yang
10. Rongjie Huang

Papers from Zi-Hua Zhang's perspective

Ordered by how strongly the researchers Zi-Hua Zhang 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. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
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

• Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling (2023) — 275 citations
• VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation (2023) — 123 citations
• Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers (2023) — 1,289 citations
• SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training (2022) — 67 citations
• SpeechLM: Enhanced Speech Pre-Training With Unpaired Textual Data (2022) — 78 citations
• Joint Pre-Training with Speech and Bilingual Text for Direct Speech to Speech Translation (2022) — 18 citations