Praveen Manoharan

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 30 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #6,606 of 35,345 by trust here, #4,336 by citations
• Safety, Robustness & Interpretability — 48 citations into it; #567 of 2,251 by trust here, #388 by citations
• Signal Processing — 25 citations into it; #1,590 of 7,572 by trust here, #459 by citations
• Computer Networks and Communications — 18 citations into it; #5,008 of 9,886 by trust here, #1,515 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Information Systems — 12 citations into it
• Statistics and Probability — 6 citations into it

Whom Praveen Manoharan trusts overall

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

1. Nicolas Papernot
2. Ian J. Goodfellow
3. Christian Szegedy
4. Geoffrey E. Hinton
5. Patrick Drew McDaniel
6. Yann LeCun
7. Alex Krizhevsky
8. Yoshua Bengio
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Praveen Manoharan overall

1. Nicholas Carlini
2. Nicolas Papernot
3. Pin-Yu Chen
4. Naveed Akhtar
5. Florian Tramèr
6. Patrick Drew McDaniel
7. Mahmood Sharif
8. A. Serban
9. David Stutz
10. Han Xu

Papers from Praveen Manoharan's perspective

Ordered by how strongly the researchers Praveen Manoharan 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Adversarial Perturbations Against Deep Neural Networks for Malware Classification (2016) — 451 citations
• On the (Statistical) Detection of Adversarial Examples (2017) — 768 citations
• Adversarial Examples for Malware Detection (2017) — 589 citations
• Membership Privacy in MicroRNA-based Studies (2016) — 155 citations