Fuming Fang

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 5 papers; trusts 89 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #11,295 of 35,345 by trust here, #7,945 by citations
• Signal Processing — 38 citations into it; #4,036 of 7,572 by trust here, #1,504 by citations
• Computer Vision and Pattern Recognition — 33 citations into it; #10,387 of 21,575 by trust here, #11,498 by citations
• Natural Language Processing — 29 citations into it
• Model Architectures — 14 citations into it
• Generative Models — 13 citations into it; #1,698 of 1,956 by trust here, #1,549 by citations
• Large Language Models — 11 citations into it
• Safety, Robustness & Interpretability — 10 citations into it

Whom Fuming Fang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Graves
5. Alex Krizhevsky
6. Yann LeCun
7. Ilya Sutskever
8. Sergey Ioffe
9. Alec Radford
10. Kaiming He

Who trusts Fuming Fang overall

1. Berrak Sisman
2. Junichi Yamagishi
3. Luisa Verdoliva
4. Wei-Ning Hsu
5. Mingyang Zhang
6. Yuezun Li
7. Kaizhi Qian
8. Songxiang Liu
9. Haizhou Li
10. Kaede Shiohara

Papers from Fuming Fang's perspective

Ordered by how strongly the researchers Fuming Fang 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos (2019) — 522 citations
• Zero-Shot Multi-Speaker Text-To-Speech with State-Of-The-Art Neural Speaker Embeddings (2019) — 204 citations
• Joint training framework for text-to-speech and voice conversion using multi-source Tacotron and WaveNet (2019) — 59 citations
• Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection (2019) — 148 citations
• High-Quality Nonparallel Voice Conversion Based on Cycle-Consistent Adversarial Network (2018) — 144 citations