Murat Kocaoglu

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 61 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #23,305 of 35,345 by trust here, #15,236 by citations
• Causal Inference & Discovery — 37 citations into it
• Statistics and Probability — 20 citations into it
• Probabilistic & Bayesian Methods — 19 citations into it; #1,495 of 1,525 by trust here, #1,415 by citations
• Computer Vision and Pattern Recognition — 18 citations into it; #14,404 of 21,575 by trust here, #11,721 by citations
• Generative Models — 17 citations into it; #1,372 of 1,956 by trust here, #1,287 by citations
• Signal Processing — 7 citations into it
• Information Systems — 6 citations into it

Whom Murat Kocaoglu trusts overall

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

1. Jiji Zhang
2. A. P. Dawid
3. Tom L. Burr
4. Geoffrey E. Hinton
5. Ian J. Goodfellow
6. Yoshua Bengio
7. Alec Radford
8. Judea Pearl
9. Alex Krizhevsky
10. Diederik P. Kingma

Who trusts Murat Kocaoglu overall

1. Christina Heinze-Deml
2. Amin Jaber
3. Álvaro Parafita
4. Yash Goyal
5. Philippe Brouillard
6. Nicolai Meinshausen
7. Sangeun Oh
8. Sébastien Lachapelle
9. Jordi Vitrià
10. Pim de Haan

Papers from Murat Kocaoglu's perspective

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

Their papers

• CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training (2017) — 314 citations
• Learning Causal Graphs with Small Interventions (2015) — 124 citations
• Contextual Bandits with Latent Confounders: An NMF Approach (2016) — 50 citations
• Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning (2020) — 131 citations
• Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions (2019) — 79 citations