Karthikeyan Shanmugam
Artificial Intelligence · 2011–2020
A researcher in the Science corpus: 11 papers; trusts 173 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 152 citations into it; #19,830 of 35,345 by trust here, #7,413 by citations
- Safety, Robustness & Interpretability — 51 citations into it
- Computer Vision and Pattern Recognition — 47 citations into it
- Causal Inference & Discovery — 26 citations into it
- Computer Networks and Communications — 23 citations into it; #7,348 of 9,886 by trust here, #2,927 by citations
- Statistics and Probability — 23 citations into it
- Computational Theory and Mathematics — 21 citations into it
- Probabilistic & Bayesian Methods — 21 citations into it; #1,401 of 1,525 by trust here, #1,415 by citations
Whom Karthikeyan Shanmugam trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- L. Eon Bottou
- Yoshua Bengio
- Alex Krizhevsky
- Jimmy Ba
- Yann LeCun
- Karen Simonyan
- Amin Shokrollahi
- Diederik P. Kingma
Who trusts Karthikeyan Shanmugam overall
- Pim de Haan
- Negin Golrezaei
- Mingyue Ji
- Zheng Chen
- Dinesh Jayaraman
- Jun Yu Li
- Imtiaz Parvez
- Giuseppe Caire
- Andreas F. Molisch
- Murat Kocaoglu
Papers from Karthikeyan Shanmugam's perspective
Ordered by how strongly the researchers Karthikeyan Shanmugam 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Invariant Risk Minimization Games (2020) — 299 citations
- Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives (2018) — 687 citations
- Learning Causal Graphs with Small Interventions (2015) — 124 citations
- Identifying Best Interventions through Online Importance Sampling (2017) — 82 citations
- Contextual Bandits with Latent Confounders: An NMF Approach (2016) — 50 citations
- FemtoCaching: Wireless Content Delivery Through Distributed Caching Helpers (2013) — 1,199 citations
- Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions (2019) — 79 citations
- One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques (2019) — 478 citations
- Improving Simple Models with Confidence Profiles (2018) — 49 citations
- TIP: Typifying the Interpretability of Procedures (2017) — 35 citations
- FemtoCaching: Wireless video content delivery through distributed caching helpers (2011) — 772 citations