Ashish Khetan

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 5 papers; trusts 151 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #15,781 of 35,345 by trust here, #15,542 by citations
• Computer Vision and Pattern Recognition — 69 citations into it; #10,413 of 21,575 by trust here, #8,409 by citations
• Generative Models — 37 citations into it; #1,233 of 1,956 by trust here, #1,163 by citations
• Natural Language Processing — 29 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Optimization & Training — 23 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Signal Processing — 11 citations into it

Whom Ashish Khetan trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. David R. Hunter
9. T. Hayes
10. Radford M. Neal

Who trusts Ashish Khetan overall

1. Jonathon Byrd
2. Giannis Karamanolakis
3. Alexander J. Ratner
4. Tian Li
5. Davood Karimi
6. Alexia Jolicoeur-Martineau
7. Ali Borji
8. Jieyu Zhang
9. Chao Lan
10. Zachary Chase Lipton

Papers from Ashish Khetan's perspective

Ordered by how strongly the researchers Ashish Khetan 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• PacGAN: The Power of Two Samples in Generative Adversarial Networks (2017) — 380 citations
• Learning From Noisy Singly-labeled Data (2017) — 170 citations
• Improving language models fine-tuning with representation consistency targets (2022)
• TabTransformer: Tabular Data Modeling Using Contextual Embeddings (2020) — 875 citations
• Computational and Statistical Tradeoffs in Learning to Rank (2016) — 15 citations