Brian Kenji Iwana

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 93 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #24,769 of 35,345 by trust here, #31,361 by citations
• Computer Vision and Pattern Recognition — 16 citations into it
• Optimization & Training — 11 citations into it; #2,016 of 2,601 by trust here, #2,428 by citations
• Signal Processing — 11 citations into it; #5,151 of 7,572 by trust here, #6,591 by citations
• Model Architectures — 8 citations into it
• Natural Language Processing — 6 citations into it
• Speech & Audio — 6 citations into it

Whom Brian Kenji Iwana trusts overall

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

1. Geoffrey E. Hinton
2. Matthew D. Zeiler
3. Karen Simonyan
4. Alex Krizhevsky
5. Sergey Ioffe
6. Sepp Hochreiter
7. Yoshua Bengio
8. Chumphol Bunkhumpornpat
9. Yann LeCun
10. Christian Szegedy

Who trusts Brian Kenji Iwana overall

1. Azal Ahmad Khan
2. Omkar Chaudhari
3. Rohitash Chandra

Papers from Brian Kenji Iwana's perspective

Ordered by how strongly the researchers Brian Kenji Iwana trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• An empirical survey of data augmentation for time series classification with neural networks (2020) — 667 citations