Carlo Luschi

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 2 papers; trusts 201 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 58 citations into it; #9,879 of 35,345 by trust here, #13,760 by citations
• Optimization & Training — 31 citations into it; #1,021 of 2,601 by trust here, #1,280 by citations
• Computer Vision and Pattern Recognition — 30 citations into it; #6,862 of 21,575 by trust here, #9,435 by citations
• Efficiency & Compression — 14 citations into it
• Model Architectures — 14 citations into it
• Natural Language Processing — 9 citations into it
• Scaling & Learning Theory — 8 citations into it

Whom Carlo Luschi trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Sergey Ioffe
6. Diederik P. Kingma
7. Yoshua Bengio
8. Karen Simonyan
9. Yann LeCun
10. X. Zhang

Who trusts Carlo Luschi overall

1. Charles H. Martin
2. Tong Yu
3. Ibrahem Kandel
4. Tonmoy Saikia
5. Yuji Roh
6. Zachary B. Charles
7. Stanislaw Jastrzebski
8. Youhui Bai
9. Michael W. Mahoney
10. Saurabh Singh

Papers from Carlo Luschi's perspective

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

Their papers

• Revisiting Small Batch Training for Deep Neural Networks (2018) — 767 citations
• Unit Scaling: Out-of-the-Box Low-Precision Training (2023) — 20 citations