Melih Kandemir

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 2 papers; trusts 271 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 30 citations into it; #23,134 of 35,345 by trust here, #19,439 by citations
• Probabilistic & Bayesian Methods — 22 citations into it; #791 of 1,525 by trust here, #865 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Optimization & Training — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it; #1,932 of 3,145 by trust here, #3,047 by citations

Whom Melih Kandemir trusts overall

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

1. Diederik P. Kingma
2. Matthew J. Beal
3. Neil D. Lawrence
4. Geoffrey E. Hinton
5. Michalis K. Titsias
6. Zoubin Ghahramani
7. Alex Krizhevsky
8. Yann LeCun
9. Kate Saenko
10. Radford M. Neal

Who trusts Melih Kandemir overall

1. Jian Shen
2. Murat Sensoy
3. Yanru Qu
4. Haitao Liu
5. Veronika Cheplygina
6. Weinan Zhang
7. Lance M. Kaplan
8. Fredrik K. Gustafsson
9. Di Feng
10. Alexander Amini

Papers from Melih Kandemir's perspective

Ordered by how strongly the researchers Melih Kandemir 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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

• Evidential Deep Learning to Quantify Classification Uncertainty (2018) — 1,595 citations
• Asymmetric Transfer Learning with Deep Gaussian Processes (2015) — 52 citations