James Qin

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 14 papers; trusts 212 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 494 citations into it; #5,854 of 35,345 by trust here, #1,242 by citations
• Speech & Audio — 230 citations into it; #339 of 1,076 by trust here, #110 by citations
• Signal Processing — 198 citations into it; #2,271 of 7,572 by trust here, #351 by citations
• Natural Language Processing — 189 citations into it
• Computer Vision and Pattern Recognition — 142 citations into it
• Model Architectures — 85 citations into it
• Representation & Self-Supervised Learning — 85 citations into it; #2,931 of 3,145 by trust here, #1,638 by citations
• Evaluation & Benchmarks — 62 citations into it

Whom James Qin trusts overall

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

1. Alex Graves
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alex Krizhevsky
6. Kaiming He
7. Ilya Sutskever
8. Jimmy Ba
9. Yann LeCun
10. Sepp Hochreiter

Who trusts James Qin overall

1. Yu Zhang
2. Jinyu Li
3. Ye Jia
4. Chung-Cheng Chiu
5. Ankur Bapna
6. Wei Han
7. Sanyuan Chen
8. Zalán Borsos
9. Yu-An Chung
10. Bo Li

Papers from James Qin's perspective

Ordered by how strongly the researchers James Qin 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• LaMDA: Language Models for Dialog Applications (2022) — 1,891 citations
• Vector-quantized Image Modeling with Improved VQGAN (2021) — 809 citations
• Conformer: Convolution-augmented Transformer for Speech Recognition (2020) — 4,195 citations
• Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling (2019) — 215 citations
• Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition (2020) — 334 citations
• ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context (2020) — 302 citations
• w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training (2021) — 560 citations
• BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition (2021) — 208 citations
• A Better and Faster end-to-end Model for Streaming ASR (2020) — 133 citations
• Scaling End-to-End Models for Large-Scale Multilingual ASR (2021) — 86 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Self-supervised Learning with Random-projection Quantizer for Speech Recognition (2022) — 258 citations
• AudioPaLM: A Large Language Model That Can Speak and Listen (2023) — 486 citations
• Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages (2023) — 396 citations