Amit Sharma

Artificial Intelligence · with Cosley · 2013–2020

A researcher in the Science corpus: 9 papers; trusts 212 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it; #9,470 of 35,345 by trust here, #11,624 by citations
• Safety, Robustness & Interpretability — 45 citations into it; #826 of 2,251 by trust here, #1,290 by citations
• Sociology and Political Science — 41 citations into it
• Computer Vision and Pattern Recognition — 39 citations into it
• Statistical and Nonlinear Physics — 37 citations into it
• Communication — 36 citations into it
• Information Systems — 34 citations into it; #5,822 of 8,771 by trust here, #3,098 by citations
• Representation & Self-Supervised Learning — 28 citations into it

Whom Amit Sharma trusts overall

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

1. Xiaoyuan Su
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yoshua Bengio
6. Eytan Bakshy
7. Kaiming He
8. Bradley Efron
9. Eric Gilbert
10. Yann LeCun

Who trusts Amit Sharma overall

1. Xiting Wang
2. Q. Liao
3. Alexandra Olteanu
4. Amir-Hossein Karimi
5. Zhaochun Ren
6. Sahil Verma
7. Zhongxia Chen
8. Yongfeng Zhang
9. Christoph Molnar
10. Carlos Castillo

Papers from Amit Sharma's perspective

Ordered by how strongly the researchers Amit Sharma 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. ImageNet Large Scale Visual Recognition Challenge
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Explaining machine learning classifiers through diverse counterfactual explanations (2019) — 1,400 citations
• Do social explanations work?: studying and modeling the effects of social explanations in recommender systems (2013) — 147 citations
• Auditing Search Engines for Differential Satisfaction Across Demographics (2017) — 90 citations
• Estimating the Causal Impact of Recommendation Systems from Observational Data (2015) — 112 citations
• Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior (2016) — 29 citations
• Domain Generalization using Causal Matching (2020) — 409 citations
• Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers (2019) — 240 citations
• Prediction and explanation in social systems (2017) — 391 citations
• Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds (2016) — 15 citations