Guneet Singh Dhillon

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 136 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #23,111 of 35,345 by trust here, #8,077 by citations
• Computer Vision and Pattern Recognition — 39 citations into it; #14,275 of 21,575 by trust here, #9,872 by citations
• Representation & Self-Supervised Learning — 20 citations into it; #2,283 of 3,145 by trust here, #1,811 by citations
• Optimization & Training — 14 citations into it
• Safety, Robustness & Interpretability — 14 citations into it
• Meta & Continual Learning — 6 citations into it
• Model Architectures — 6 citations into it

Whom Guneet Singh Dhillon trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yoshua Bengio
6. J. Urgen Schmidhuber
7. Christian Szegedy
8. Yann LeCun
9. Ian J. Goodfellow
10. Ilya Sutskever

Who trusts Guneet Singh Dhillon overall

1. Renrui Zhang
2. Wei-Hong Li
3. Steinar Laenen
4. Cuong V Nguyen
5. Zhongqi Yue
6. Yonglong Tian
7. Dequan Wang
8. A. Serban
9. Lu Liu
10. Carlos Riquelme

Papers from Guneet Singh Dhillon's perspective

Ordered by how strongly the researchers Guneet Singh Dhillon 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Baseline for Few-Shot Image Classification (2019) — 646 citations
• Stochastic Activation Pruning for Robust Adversarial Defense (2018) — 581 citations