Yigitcan Kaya

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 203 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 115 citations into it
• Safety, Robustness & Interpretability — 79 citations into it
• Computer Vision and Pattern Recognition — 50 citations into it
• Computer Networks and Communications — 23 citations into it
• Signal Processing — 22 citations into it
• Efficiency & Compression — 16 citations into it
• Optimization & Training — 13 citations into it; #2,478 of 2,601 by trust here, #1,986 by citations
• Model Architectures — 11 citations into it; #2,039 of 2,278 by trust here, #1,882 by citations

Whom Yigitcan Kaya trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Christian Szegedy
6. Karen Simonyan
7. Yann LeCun
8. Yoshua Bengio
9. Ilya Sutskever
10. Jimmy Ba

Who trusts Yigitcan Kaya overall

1. Sanghyun Hong
2. Simone Scardapane
3. Stefanos Laskaridis
4. Yizeng Han
5. Xipeng Qiu
6. Sanyuan Chen
7. Wangchunshu Zhou
8. Canwen Xu
9. Michele Scarpiniti
10. Stylianos I. Venieris

Papers from Yigitcan Kaya's perspective

Ordered by how strongly the researchers Yigitcan Kaya 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks (2018) — 323 citations
• On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping (2020) — 157 citations
• A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference (2020) — 88 citations
• Shallow-Deep Networks: Understanding and Mitigating Network Overthinking (2018) — 395 citations