Shailesh Tripathi

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 33 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it
• Computer Vision and Pattern Recognition — 32 citations into it
• Model Architectures — 23 citations into it
• Natural Language Processing — 13 citations into it
• Signal Processing — 9 citations into it
• Optimization & Training — 8 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Shailesh Tripathi trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. Yann LeCun
5. Paul J. Werbos
6. J. Urgen Schmidhuber
7. Sepp Hochreiter
8. Alex Krizhevsky
9. L. Eon Bottou
10. Diederik P. Kingma

Papers from Shailesh Tripathi's perspective

Ordered by how strongly the researchers Shailesh Tripathi 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Going deeper with convolutions
9. ImageNet: A large-scale hierarchical image database
10. GENERATIVE ADVERSARIAL NETS

Their papers

• An Introductory Review of Deep Learning for Prediction Models With Big Data (2020) — 492 citations