Babak Shakibi

Artificial Intelligence · 2013–2014

A researcher in the Science corpus: 2 papers; trusts 23 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 28 citations into it; #10,162 of 35,345 by trust here, #3,783 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Optimization & Training — 12 citations into it; #1,248 of 2,601 by trust here, #385 by citations
• Management Science and Operations Research — 10 citations into it; #7,558 of 11,946 by trust here, #7,638 by citations
• Model Architectures — 9 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Computational Theory and Mathematics — 7 citations into it; #8,120 of 12,182 by trust here, #7,501 by citations
• Reinforcement Learning — 7 citations into it

Whom Babak Shakibi trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Yann LeCun
5. Nando de Freitas
6. R´emi Munos
7. Marc'Aurelio Ranzato
8. Ilya Sutskever
9. Andrew Y. Ng
10. Diederik P. Kingma

Who trusts Babak Shakibi overall

1. Wujie Wen
2. Jakub Konecný
3. M. A. Carreira-Perpiñán
4. Yerlan Idelbayev
5. Grace W. Lindsay
6. James T. Wilson
7. Pratik Chaudhari
8. Suraj Srinivas
9. José Manuel Álvarez
10. Waseem Rawat

Papers from Babak Shakibi's perspective

Ordered by how strongly the researchers Babak Shakibi 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Predicting Parameters in Deep Learning (2013) — 1,410 citations
• Bayesian Multi-Scale Optimistic Optimization (2014) — 95 citations