J. Ash

Artificial Intelligence · with Langford · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 183 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #20,436 of 35,345 by trust here, #20,836 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #21,006 of 21,575 by trust here, #18,782 by citations
• Optimization & Training — 35 citations into it
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Management Science and Operations Research — 6 citations into it

Whom J. Ash trusts overall

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

1. Kaiming He
2. Yoshua Bengio
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Yann LeCun
7. Ilya Sutskever
8. Christian Szegedy
9. Karen Simonyan
10. X. Zhang

Who trusts J. Ash overall

1. Krishnateja Killamsetty
2. Mohamed Elsayed
3. A. Rupam Mahmood
4. Eugene Belilovsky
5. Durga Sivasubramanian
6. Pingbo Pan
7. Pengzhen Ren
8. Atsushi Nitanda
9. Yigitcan Kaya
10. Ganesh Ramakrishnan

Papers from J. Ash's perspective

Ordered by how strongly the researchers J. Ash 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. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Learning Deep ResNet Blocks Sequentially using Boosting Theory (2017) — 120 citations
• Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds (2019) — 1,024 citations
• On Warm-Starting Neural Network Training (2019) — 266 citations