Shubham Sharma

Artificial Intelligence · with Ghosh · 2019

A researcher in the Science corpus: 3 papers; trusts 31 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #19,205 of 35,345 by trust here, #14,181 by citations
• Safety, Robustness & Interpretability — 57 citations into it; #946 of 2,251 by trust here, #1,880 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Safety Research — 18 citations into it
• AI Ethics, Fairness & Society — 14 citations into it; #157 of 494 by trust here, #141 by citations
• Optimization & Training — 7 citations into it
• Causal Inference & Discovery — 6 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Shubham Sharma trusts overall

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

1. Zhou Wang
2. Chris Russell
3. Berk Ustun
4. Sandra Wachter
5. Ron Kohavi
6. Reuben Binns
7. Riccardo Guidotti
8. Christian Szegedy
9. A. Bovik
10. Tianqi Chen

Who trusts Shubham Sharma overall

1. Susanne Dandl
2. Amir-Hossein Karimi
3. Sahil Verma
4. Christoph Molnar
5. Bernhard Schölkopf
6. Anna Markella Antoniadi
7. John P. Dickerson
8. Alejandro Barredo Arrieta
9. Martin Binder
10. Umang Bhatt

Papers from Shubham Sharma's perspective

Ordered by how strongly the researchers Shubham Sharma trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Explainable machine learning in deployment (2019) — 701 citations
• CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models (2019) — 192 citations
• CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models (2019) — 102 citations