Zhouhan Lin
Artificial Intelligence · 2014–2021
A researcher in the Science corpus: 12 papers; trusts 296 researchers and is trusted by 49 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 202 citations into it; #8,623 of 35,345 by trust here, #2,774 by citations
- Computer Vision and Pattern Recognition — 113 citations into it; #9,427 of 21,575 by trust here, #5,116 by citations
- Natural Language Processing — 109 citations into it; #3,226 of 5,688 by trust here, #896 by citations
- Model Architectures — 65 citations into it; #926 of 2,278 by trust here, #197 by citations
- Optimization & Training — 38 citations into it
- Media Technology — 31 citations into it; #1,096 of 3,621 by trust here, #385 by citations
- Representation & Self-Supervised Learning — 30 citations into it; #1,946 of 3,145 by trust here, #633 by citations
- Reinforcement Learning — 29 citations into it
Whom Zhouhan Lin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Alex Graves
- Ruslan Salakhutdinov
- Sepp Hochreiter
Who trusts Zhouhan Lin overall
- Qian Chen
- Han-Jia Ye
- Fengfu Li
- Huan Wang
- Yoshua Bengio
- Xipeng Qiu
- Derya Soydaner
- Asit K. Mishra
- Dichao Hu
- Itay Hubara
Papers from Zhouhan Lin's perspective
Ordered by how strongly the researchers Zhouhan Lin trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A Structured Self-attentive Sentence Embedding (2017) — 2,310 citations
- Towards Biologically Plausible Deep Learning (2015) — 383 citations
- A Deep Reinforcement Learning Chatbot (2017) — 245 citations
- Architectural Complexity Measures of Recurrent Neural Networks (2016) — 164 citations
- How far can we go without convolution: Improving fully-connected networks (2015) — 57 citations
- Recurrent Neural Networks With Limited Numerical Precision (2016) — 80 citations
- Focused Hierarchical RNNs for Conditional Sequence Processing (2018) — 25 citations
- Spatial-Spectral Transformer for Hyperspectral Image Classification (2021) — 399 citations
- The Octopus Approach to the Alexa Competition : A Deep Ensemble-based Socialbot (2017)
- Theano: A Python framework for fast computation of mathematical expressions (2016) — 2,380 citations
- Neural Networks with Few Multiplications (2015) — 347 citations
- Deep Learning-Based Classification of Hyperspectral Data (2014) — 2,291 citations