Nutan Chen

Artificial Intelligence · 2013–2019

A researcher in the Science corpus: 3 papers; trusts 72 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #24,202 of 35,345 by trust here, #13,353 by citations
• Computer Vision and Pattern Recognition — 31 citations into it
• Generative Models — 19 citations into it
• Reinforcement Learning — 17 citations into it
• Optimization & Training — 15 citations into it
• Control and Systems Engineering — 13 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Representation & Self-Supervised Learning — 12 citations into it

Whom Nutan Chen trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Graves
4. Diederik P. Kingma
5. Ilya Sutskever
6. Radford M. Neal
7. Yann LeCun
8. Alex Krizhevsky
9. Ruslan Salakhutdinov
10. Danilo Jimenez Rezende

Who trusts Nutan Chen overall

1. Yarin Gal
2. Fei Ye
3. Timothée Lesort
4. Justin Bayer
5. Louay Hazami
6. Debidatta Dwibedi
7. Jyoti Aneja
8. Marvin Zhang
9. Ali Ghadirzadeh

Papers from Nutan Chen's perspective

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

Their papers

• On Fast Dropout and its Applicability to Recurrent Networks (2013) — 66 citations
• Stable reinforcement learning with autoencoders for tactile and visual data (2016) — 185 citations
• Learning Hierarchical Priors in VAEs (2019) — 110 citations