Kin Gwn Lore

Computer Vision and Pattern Recognition · 2015

A researcher in the Science corpus: 1 paper; trusts 14 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 19 citations into it; #6,051 of 21,575 by trust here, #9,187 by citations

Whom Kin Gwn Lore trusts overall

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

1. Alex Krizhevsky
2. Kostadin Dabov
3. Elmar Eisemann
4. Junyuan Xie
5. Raymond H. Chan
6. Geoffrey E. Hinton
7. Christian J. Schuler
8. Amit K. Agrawal
9. Ilya Sutskever
10. Yoshua Bengio

Who trusts Kin Gwn Lore overall

1. Wenhan Yang
2. Chongyi Li
3. Wenjing Wang
4. Jiaying Liu
5. Chunle Guo
6. Chen Wei
7. Yuen Peng Loh
8. Qifeng Chen
9. Feifan Lv
10. Adil Rasheed

Papers from Kin Gwn Lore's perspective

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

Their papers

• LLNet: A deep autoencoder approach to natural low-light image enhancement (2015) — 1,822 citations