Murat Sensoy

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 71 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #22,484 of 35,345 by trust here, #10,430 by citations
• Computer Vision and Pattern Recognition — 39 citations into it
• Probabilistic & Bayesian Methods — 33 citations into it; #1,167 of 1,525 by trust here, #921 by citations
• Safety, Robustness & Interpretability — 29 citations into it; #1,518 of 2,251 by trust here, #999 by citations
• Model Architectures — 9 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Statistics and Probability — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Murat Sensoy trusts overall

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

1. Diederik P. Kingma
2. Yarin Gal
3. Yann LeCun
4. Ian J. Goodfellow
5. Geoffrey E. Hinton
6. Radford M. Neal
7. Alex Krizhevsky
8. Max Welling
9. Yoshua Bengio
10. Zoubin Ghahramani

Who trusts Murat Sensoy overall

1. Fredrik K. Gustafsson
2. Di Feng
3. Alexander Amini
4. Zongbo Han
5. Jeremiah Zhe Liu
6. Eyke Hüllermeier
7. Lance M. Kaplan
8. Federico Cerutti
9. Jakob Gawlikowski
10. Alun David Preece

Papers from Murat Sensoy's perspective

Ordered by how strongly the researchers Murat Sensoy 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Interpretability of deep learning models: A survey of results (2017) — 469 citations
• Uncertainty-Aware Deep Classifiers Using Generative Models (2020) — 90 citations
• Uncertainty-aware situational understanding (2019) — 3 citations
• Evidential Deep Learning to Quantify Classification Uncertainty (2018) — 1,595 citations
• Uncertainty Aware AI ML: Why and How (2018) — 22 citations