Ziwei Ji

Artificial Intelligence · with Telgarsky · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 18 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 18 citations into it; #10,173 of 35,345 by trust here, #14,438 by citations
• Optimization & Training — 18 citations into it; #1,283 of 2,601 by trust here, #1,488 by citations
• Computational Mechanics — 11 citations into it; #2,583 of 10,001 by trust here, #2,782 by citations
• Scaling & Learning Theory — 8 citations into it; #328 of 428 by trust here, #374 by citations
• Computational Theory and Mathematics — 5 citations into it

Whom Ziwei Ji trusts overall

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

1. Yurii E. Nesterov
2. Sébastien Bubeck
3. Alex Krizhevsky
4. Yoav Freund
5. Francis R. Bach
6. Daniel Soudry
7. Robert E. Schapire
8. Elad Hazan
9. Jonathan Michael Borwein
10. Suriya Gunasekar

Who trusts Ziwei Ji overall

1. Kaifeng Lyu
2. Colin Wei
3. Samet Oymak
4. Sanjeev Arora
5. Wei Hu
6. Micah Goldblum
7. Vidya Muthukumar
8. Daniel Soudry
9. Jian Li
10. Simon Shaolei Du

Papers from Ziwei Ji's perspective

Ordered by how strongly the researchers Ziwei Ji 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. Learning Multiple Layers of Features from Tiny Images
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
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

• Gradient descent aligns the layers of deep linear networks (2018) — 304 citations
• Risk and parameter convergence of logistic regression (2018) — 142 citations
• The implicit bias of gradient descent on nonseparable data (2019) — 208 citations