Long Zhou
Artificial Intelligence · 2019–2024
A researcher in the Science corpus: 14 papers; trusts 159 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 514 citations into it; #26,026 of 35,345 by trust here, #3,384 by citations
- Signal Processing — 261 citations into it
- Speech & Audio — 246 citations into it
- Natural Language Processing — 197 citations into it
- Information Systems — 97 citations into it; #4,676 of 8,771 by trust here, #320 by citations
- Computer Vision and Pattern Recognition — 96 citations into it
- Representation & Self-Supervised Learning — 69 citations into it
- Large Language Models — 67 citations into it
Whom Long Zhou trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Graves
- Ilya Sutskever
- Jimmy Ba
- Alex Krizhevsky
- Kaiming He
- Oriol Vinyals
- Ashish Vaswani
Who trusts Long Zhou overall
- Zi-Hua Zhang
- Dong Zhang
- Sanyuan Chen
- Krishna C. Puvvada
- Shujie Liu
- Junyi Ao
- Ziyue Jiang
- Evgeny Kharitonov
- Yue Wang
- Yushen Chen
Papers from Long Zhou's perspective
Ordered by how strongly the researchers Long Zhou trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- GraphCodeBERT: Pre-training Code Representations with Data Flow (2020) — 1,778 citations
- WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing (2021) — 3,371 citations
- 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
- SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2021) — 297 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
- VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers (2024) — 206 citations
- SpeechLM: Enhanced Speech Pre-Training With Unpaired Textual Data (2022) — 78 citations
- Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding (2019) — 73 citations
- Joint Pre-Training with Speech and Bilingual Text for Direct Speech to Speech Translation (2022) — 18 citations
- SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation (2021) — 14 citations
- CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation (2021) — 1,545 citations
- CodeBLEU: a Method for Automatic Evaluation of Code Synthesis (2020) — 902 citations