Sreeram Kannan

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 68 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #13,766 of 35,345 by trust here, #7,858 by citations
• Computer Vision and Pattern Recognition — 29 citations into it; #19,013 of 21,575 by trust here, #19,766 by citations
• Generative Models — 15 citations into it
• Optimization & Training — 12 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Safety, Robustness & Interpretability — 12 citations into it; #1,344 of 2,251 by trust here, #1,186 by citations
• Molecular Biology — 9 citations into it
• Statistics and Probability — 8 citations into it

Whom Sreeram Kannan trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yann LeCun
6. Christian Szegedy
7. Ian J. Goodfellow
8. Ilya Sutskever
9. Liam Paninski
10. Franccois Fleuret

Who trusts Sreeram Kannan overall

1. Joni O. Salminen
2. Svetlana Kiritchenko
3. Alexander A. Alemi
4. Morteza Noshad
5. Hossein Hosseini
6. Isar Nejadgholi
7. Zhijin Qin
8. Motoki Sato
9. Yotam Gil
10. Ethan Perez

Papers from Sreeram Kannan's perspective

Ordered by how strongly the researchers Sreeram Kannan 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. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deceiving Google's Perspective API Built for Detecting Toxic Comments (2017) — 369 citations
• ClusterGAN : Latent Space Clustering in Generative Adversarial Networks (2018) — 384 citations
• Estimating Mutual Information for Discrete-Continuous Mixtures (2017) — 184 citations
• Improving Federated Learning Personalization via Model Agnostic Meta Learning (2019) — 688 citations