Supriyo Chakraborty

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 15 papers; trusts 363 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 277 citations into it; #9,424 of 35,345 by trust here, #4,577 by citations
• Safety, Robustness & Interpretability — 140 citations into it; #1,403 of 2,251 by trust here, #593 by citations
• Computer Vision and Pattern Recognition — 86 citations into it; #17,669 of 21,575 by trust here, #15,529 by citations
• Sociology and Political Science — 38 citations into it; #9,774 of 22,756 by trust here, #18,468 by citations
• Privacy & Security — 28 citations into it; #615 of 709 by trust here, #670 by citations
• Natural Language Processing — 27 citations into it
• Information Systems — 22 citations into it
• Signal Processing — 22 citations into it; #6,154 of 7,572 by trust here, #5,570 by citations

Whom Supriyo Chakraborty trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. Diederik P. Kingma
6. Cynthia Dwork
7. Ilya Sutskever
8. L. Eon Bottou
9. Kaiming He
10. Sepp Hochreiter

Who trusts Supriyo Chakraborty overall

1. Alun David Preece
2. Chong Huang
3. Virat Shejwalkar
4. Peter Kairouz
5. Sari Sabban
6. Samuel Budd
7. David Enthoven
8. Changchang Liu
9. Dang Lien Minh
10. Xiaoyu Cao

Papers from Supriyo Chakraborty's perspective

Ordered by how strongly the researchers Supriyo Chakraborty 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Interpretability of deep learning models: A survey of results (2017) — 469 citations
• Analyzing Federated Learning through an Adversarial Lens (2018) — 1,351 citations
• DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017) — 27 citations
• Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016) — 213 citations
• Sanity Checks for Saliency Metrics (2019) — 210 citations
• IBM Federated Learning: an Enterprise Framework White Paper V0.1 (2020) — 198 citations
• Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI (2020) — 175 citations
• Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models (2020) — 16 citations
• Supplementary : Analyzing Federated Learning through an Adversarial Lens (2019)
• Fair Transfer Learning with Missing Protected Attributes (2019) — 123 citations
• Stakeholders in Explainable AI (2018) — 183 citations
• Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems (2018) — 191 citations
• Cognitive computing for coalition situational understanding (2017) — 20 citations
• Deep learning for situational understanding (2017) — 15 citations
• SenseGen: A deep learning architecture for synthetic sensor data generation (2017) — 143 citations