Meng Joo Er

Artificial Intelligence · 2000–2017

A researcher in the Science corpus: 8 papers; trusts 44 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #6,079 of 35,345 by trust here, #15,004 by citations
• Computer Vision and Pattern Recognition — 29 citations into it; #11,952 of 21,575 by trust here, #11,498 by citations
• Reinforcement Learning — 18 citations into it
• Information Systems — 12 citations into it
• Molecular Biology — 10 citations into it
• Control and Systems Engineering — 9 citations into it; #953 of 8,257 by trust here, #1,475 by citations
• Model Architectures — 7 citations into it
• Computational Theory and Mathematics — 6 citations into it

Whom Meng Joo Er trusts overall

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

1. Shiqian Wu
2. Peter N. Belhumeur
3. Andrew Y. Ng
4. Pieter Abbeel
5. Charles M. Bishop
6. Sukhan Lee
7. Nathan D. Ratliff
8. Martin L. Puterman
9. Hua Yu
10. João Pedra Hespanha

Who trusts Meng Joo Er overall

1. Edwin David Lughofer
2. Shiqian Wu
3. Eugen Diaconescu
4. Plamen Parvanov Angelov
5. Yang Gao
6. Daniel S. Brown
7. Chia-Feng Juang
8. Andrea Francesco Abate
9. Dunhui Xiao
10. Péter Englert

Papers from Meng Joo Er's perspective

Ordered by how strongly the researchers Meng Joo Er 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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. GENERATIVE ADVERSARIAL NETS

Their papers

• A survey of inverse reinforcement learning techniques (2012) — 116 citations
• A review of clustering techniques and developments (2017) — 1,203 citations
• Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain (2006) — 618 citations
• High-speed face recognition based on discrete cosine transform and RBF neural networks (2005) — 353 citations
• NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches (2005) — 246 citations
• Face recognition with radial basis function (RBF) neural networks (2002) — 729 citations
• A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks (2001) — 415 citations
• Dynamic fuzzy neural networks-a novel approach to function approximation (2000) — 468 citations