M. S. Kaiser

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 169 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it
• Computer Vision and Pattern Recognition — 68 citations into it
• Molecular Biology — 49 citations into it
• Cognitive Neuroscience — 28 citations into it
• Model Architectures — 28 citations into it
• Radiology, Nuclear Medicine and Imaging — 18 citations into it
• Reinforcement Learning — 18 citations into it
• Signal Processing — 13 citations into it

Whom M. S. Kaiser trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Yann LeCun
5. Alex Krizhevsky
6. Richard S. Sutton
7. Ruslan Salakhutdinov
8. Ilya Sutskever
9. Karen Simonyan
10. J. Urgen Schmidhuber

Who trusts M. S. Kaiser overall

1. Oludare Isaac Abiodun

Papers from M. S. Kaiser's perspective

Ordered by how strongly the researchers M. S. Kaiser 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Learning in Mining Biological Data (2020) — 333 citations
• Applications of Deep Learning and Reinforcement Learning to Biological Data (2017) — 707 citations