Bimal Viswanath
Artificial Intelligence · 2009–2019
A researcher in the Science corpus: 9 papers; trusts 72 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 132 citations into it
- Safety, Robustness & Interpretability — 59 citations into it
- Information Systems — 48 citations into it; #3,318 of 8,771 by trust here, #1,867 by citations
- Computer Networks and Communications — 39 citations into it; #2,282 of 9,886 by trust here, #1,617 by citations
- Signal Processing — 39 citations into it; #3,279 of 7,572 by trust here, #785 by citations
- Computer Vision and Pattern Recognition — 38 citations into it
- Statistical and Nonlinear Physics — 35 citations into it; #3,179 of 10,127 by trust here, #1,239 by citations
- Natural Language Processing — 21 citations into it
Whom Bimal Viswanath trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Mark Newman
- Aaron Clauset
- Geoffrey E. Hinton
- Diederik P. Kingma
- Jon M. Kleinberg
- Yoshua Bengio
- George Danezis
- Alex Graves
- Alex Krizhevsky
- Ilya Sutskever
Who trusts Bimal Viswanath overall
- Binghui Wang
- Neil Zhenqiang Gong
- David M. Beskow
- Lorenzo Alvisi
- Gang Wang
- Prateek Mittal
- Peng Gao
- Le Zhang
- Changchang Liu
- Jinyuan Jia
Papers from Bimal Viswanath's perspective
Ordered by how strongly the researchers Bimal Viswanath trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks (2019) — 1,894 citations
- Understanding and combating link farming in the twitter social network (2012) — 409 citations
- Automated Crowdturfing Attacks and Defenses in Online Review Systems (2017) — 169 citations
- With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning (2018) — 126 citations
- Strength in Numbers: Robust Tamper Detection in Crowd Computations (2015) — 46 citations
- Towards Detecting Anomalous User Behavior in Online Social Networks (2014) — 299 citations
- An analysis of social network-based Sybil defenses (2010) — 421 citations
- You are who you know: inferring user profiles in online social networks (2010) — 796 citations
- On the evolution of user interaction in Facebook (2009) — 1,561 citations