Sunil Thulasidasan

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 77 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Safety, Robustness & Interpretability — 19 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Natural Language Processing — 9 citations into it
• Model Architectures — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Sunil Thulasidasan trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Karen Simonyan
5. Yoshua Bengio
6. Christian Szegedy
7. Jia Deng
8. Diederik P. Kingma
9. Yann LeCun
10. Ross B. Girshick

Who trusts Sunil Thulasidasan overall

1. Jingkang Yang
2. Aviral Kumar
3. Sudhanshu Mittal
4. Lilly Kumari
5. Mamshad Nayeem Rizve
6. Sungnyun Kim
7. David Macêdo
8. Eric Arazo
9. Yezhen Wang
10. Zicheng Liu

Papers from Sunil Thulasidasan's perspective

Ordered by how strongly the researchers Sunil Thulasidasan 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Combating Label Noise in Deep Learning Using Abstention (2019) — 196 citations
• On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks (2019) — 628 citations