Shobha Venkataraman

Artificial Intelligence · 2002–2021

A researcher in the Science corpus: 6 papers; trusts 21 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #11,378 of 35,345 by trust here, #12,387 by citations
• Computer Networks and Communications — 34 citations into it; #5,707 of 9,886 by trust here, #4,950 by citations
• Information Systems — 28 citations into it; #4,772 of 8,771 by trust here, #5,303 by citations
• Signal Processing — 26 citations into it; #4,554 of 7,572 by trust here, #4,560 by citations
• Privacy & Security — 19 citations into it
• Computational Theory and Mathematics — 11 citations into it; #3,258 of 12,182 by trust here, #2,298 by citations
• Reinforcement Learning — 10 citations into it; #725 of 2,242 by trust here, #992 by citations

Whom Shobha Venkataraman trusts overall

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

1. Nick Littlestone
2. Daphne Koller
3. James Newsome
4. Cynthia Dwork
5. Geoffrey E. Hinton
6. Carlos Guestrin
7. Payman Mohassel
8. Yann LeCun
9. Kaiming He
10. Alex Krizhevsky

Who trusts Shobha Venkataraman overall

1. Blaine Nelson
2. Jelle R. Kok
3. Yanfang Ye
4. Benjamin I. P. Rubinstein
5. Ling Huang
6. Yujing Hu
7. Carlos Guestrin
8. Wendelin Böhmer
9. Marc Toussaint
10. Nikos A. Vlassis

Papers from Shobha Venkataraman's perspective

Ordered by how strongly the researchers Shobha Venkataraman 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. Gradient-based learning applied to document recognition
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Efficient Solution Algorithms for Factored MDPs (2003) — 570 citations
• Identifying diverse usage behaviors of smartphone apps (2011) — 465 citations
• CrypTen: Secure Multi-Party Computation Meets Machine Learning (2021) — 544 citations
• BitShred: feature hashing malware for scalable triage and semantic analysis (2011) — 304 citations
• Limits of Learning-based Signature Generation with Adversaries (2008) — 63 citations
• Context-specific multiagent coordination and planning with factored MDPs (2002) — 138 citations