I-Fan Chen

Artificial Intelligence · 2014–2015

A researcher in the Science corpus: 3 papers; trusts 28 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #16,935 of 35,345 by trust here, #19,018 by citations
• Signal Processing — 38 citations into it; #3,396 of 7,572 by trust here, #3,309 by citations
• Speech & Audio — 18 citations into it
• Natural Language Processing — 13 citations into it
• Model Architectures — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Efficiency & Compression — 5 citations into it
• Optimization & Training — 5 citations into it

Whom I-Fan Chen trusts overall

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

1. Geoffrey E. Hinton
2. Dong Yu
3. Chris Leggetter
4. Jinyu Li
5. Frank Torsten Bernd Seide
6. Radford M. Neal
7. Tara N. Sainath
8. Hervé Bourlard
9. David E. Rumelhart
10. Ofer Dekel

Who trusts I-Fan Chen overall

1. Jinyu Li
2. Zhen Huang
3. Zhong Meng
4. Sang-Woo Lee

Papers from I-Fan Chen's perspective

Ordered by how strongly the researchers I-Fan Chen 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Rapid adaptation for deep neural networks through multi-task learning (2015) — 69 citations
• Maximum a posteriori adaptation of network parameters in deep models (2015) — 54 citations
• Feature space maximum a posteriori linear regression for adaptation of deep neural networks (2014) — 21 citations