Ursula Challita

Electrical and Electronic Engineering · 2017–2018

A researcher in the Science corpus: 5 papers; trusts 89 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 103 citations into it; #3,232 of 16,539 by trust here, #5,149 by citations
• Artificial Intelligence — 96 citations into it; #20,064 of 35,345 by trust here, #11,750 by citations
• Computer Networks and Communications — 96 citations into it; #3,449 of 9,886 by trust here, #2,927 by citations
• Computer Vision and Pattern Recognition — 42 citations into it
• Aerospace Engineering — 30 citations into it; #4,659 of 6,421 by trust here, #5,612 by citations
• Optimization & Training — 27 citations into it
• Model Architectures — 24 citations into it
• Signal Processing — 19 citations into it

Whom Ursula Challita trusts overall

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

1. Ronald J. Williams
2. Richard S. Sutton
3. Geoffrey E. Hinton
4. J. Urgen Schmidhuber
5. Ilya Sutskever
6. Diederik P. Kingma
7. Paul J. Werbos
8. Alex Graves
9. Alex Krizhevsky
10. Sepp Hochreiter

Who trusts Ursula Challita overall

1. Mingzhe Chen
2. Jihong Park
3. Walid Saad
4. Yong Zeng
5. Szilárd Aradi
6. Nan Zhao
7. Merouane Debbah
8. Medhi Bennis
9. Sumudu Samarakoon
10. Qingqing Wu

Papers from Ursula Challita's perspective

Ordered by how strongly the researchers Ursula Challita 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017) — 871 citations
• Interference Management for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach (2018) — 336 citations
• Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017) — 226 citations
• Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective (2017) — 167 citations
• Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems (2018) — 104 citations