Khalid Ashraf

Computer Vision and Pattern Recognition · 2015–2017

A researcher in the Science corpus: 4 papers; trusts 310 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 84 citations into it; #9,177 of 21,575 by trust here, #6,031 by citations
• Artificial Intelligence — 62 citations into it; #7,016 of 35,345 by trust here, #1,914 by citations
• Model Architectures — 32 citations into it
• Optimization & Training — 22 citations into it
• Efficiency & Compression — 18 citations into it; #234 of 1,264 by trust here, #57 by citations
• Aerospace Engineering — 8 citations into it
• Computer Networks and Communications — 8 citations into it
• Electrical and Electronic Engineering — 8 citations into it

Whom Khalid Ashraf trusts overall

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

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

Who trusts Khalid Ashraf overall

1. Bichen Wu
2. Yang You
3. Zi-Mei Yao
4. Janis Keuper
5. Forrest N. Iandola
6. Song Han
7. Linfeng Zhang
8. Jianyu Wang
9. Tian Guo
10. Haichuan Yang

Papers from Khalid Ashraf's perspective

Ordered by how strongly the researchers Khalid Ashraf 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size (2016) — 8,474 citations
• FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters (2015) — 312 citations
• - LEVEL ACCURACY WITH 50 X FEWER PARAMETERS AND < 0 . 5 MB MODEL SIZE (2016)
• Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks (2017) — 207 citations