Bobak Toussi Kiani

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 2 papers; trusts 40 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it
• Scaling & Learning Theory — 35 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Optimization & Training — 12 citations into it
• Atomic and Molecular Physics, and Optics — 11 citations into it
• Statistical Learning Theory — 9 citations into it
• Computational Theory and Mathematics — 8 citations into it
• Model Architectures — 8 citations into it

Whom Bobak Toussi Kiani trusts overall

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

1. Diederik P. Kingma
2. Yann LeCun
3. Jorma Rissanen
4. Yoshua Bengio
5. J. Urgen Schmidhuber
6. Geoffrey E. Hinton
7. Temple F. Smith
8. Jimmy Ba
9. Yuanzhi Li
10. L. Eon Bottou

Papers from Bobak Toussi Kiani's perspective

Ordered by how strongly the researchers Bobak Toussi Kiani trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Gradient-based learning applied to document recognition
3. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Quantum variational algorithms are swamped with traps (2022) — 318 citations
• Deep neural networks are biased towards simple functions (2018) — 73 citations