Frank Kagan Gürkaynak

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 25 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 39 citations into it
• Artificial Intelligence — 35 citations into it
• Electrical and Electronic Engineering — 25 citations into it; #14,380 of 16,539 by trust here, #11,641 by citations
• Hardware and Architecture — 16 citations into it; #1,375 of 1,551 by trust here, #1,043 by citations
• Efficiency & Compression — 14 citations into it
• Computer Networks and Communications — 13 citations into it
• Model Architectures — 12 citations into it
• Optimization & Training — 9 citations into it

Whom Frank Kagan Gürkaynak trusts overall

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

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

Who trusts Frank Kagan Gürkaynak overall

1. Luca Benini

Papers from Frank Kagan Gürkaynak's perspective

Ordered by how strongly the researchers Frank Kagan Gürkaynak 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. Going deeper with convolutions
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Attention is All you Need (Neural Information Processing Systems)
10. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Their papers

• Near-Threshold RISC-V Core With DSP Extensions for Scalable IoT Endpoint Devices (2017) — 69 citations
• A near-threshold RISC-V core with DSP extensions for scalable IoT Endpoint Devices (2016) — 409 citations
• An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics (2016) — 122 citations
• A Scalable Near-Memory Architecture for Training Deep Neural Networks on Large In-Memory Datasets (2018) — 73 citations