Inci M. Baytas

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 37 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #16,314 of 35,345 by trust here, #14,718 by citations
• Computational Mechanics — 37 citations into it; #4,445 of 10,001 by trust here, #4,600 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Optimization & Training — 18 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Statistics and Probability — 12 citations into it
• Privacy & Security — 8 citations into it
• Computer Networks and Communications — 7 citations into it

Whom Inci M. Baytas trusts overall

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

1. Yoshua Bengio
2. Cynthia Dwork
3. Jiayu Zhou
4. Geoffrey E. Hinton
5. Paul Tseng
6. Jieping Ye
7. Stephen J. Wright
8. Alex Krizhevsky
9. Ray R. Larson
10. Shuiwang Ji

Who trusts Inci M. Baytas overall

1. Yujing Chen
2. Ming Yan
3. Mohammad Rostami
4. Sulin Liu
5. Yue Ning
6. Cao Xiao
7. Srijan Kumar
8. Wentai Wu
9. Ramit Sawhney
10. Yu Zhang

Papers from Inci M. Baytas's perspective

Ordered by how strongly the researchers Inci M. Baytas 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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

• Asynchronous Multi-task Learning (2016) — 47 citations
• Patient Subtyping via Time-Aware LSTM Networks (2017) — 702 citations
• Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates (2017) — 77 citations
• Multi-Task Feature Interaction Learning (2016) — 33 citations