Noah Golowich
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 7 papers; trusts 66 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 212 citations into it; #6,441 of 35,345 by trust here, #6,330 by citations
- Privacy & Security — 108 citations into it; #326 of 709 by trust here, #414 by citations
- Optimization & Training — 56 citations into it; #2,126 of 2,601 by trust here, #1,866 by citations
- Computational Theory and Mathematics — 45 citations into it
- Statistics and Probability — 28 citations into it; #12,745 of 12,932 by trust here, #10,275 by citations
- Computer Vision and Pattern Recognition — 26 citations into it; #16,428 of 21,575 by trust here, #14,478 by citations
- Computational Mechanics — 24 citations into it; #9,729 of 10,001 by trust here, #8,566 by citations
- Algorithms & Complexity — 22 citations into it
Whom Noah Golowich trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Cynthia Dwork
- Arkadi Nemirovski
- Peter L. Bartlett
- Yurii E. Nesterov
- Ú. Erlingsson
- Gauthier Gidel
- Waïss Azizian
- Arkadi S. Nemirovsky
- Geoffrey E. Hinton
- Shai Shalev-Shwartz
Who trusts Noah Golowich overall
- Badih Ghazi
- Colin Wei
- Victor Balcer
- Albert Cheu
- Pasin Manurangsi
- Nilesh Tripuraneni
- Ravi Kumar
- Samet Oymak
- Zeyuan Allen-Zhu
- Simon Shaolei Du
Papers from Noah Golowich's perspective
Ordered by how strongly the researchers Noah Golowich trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- GENERATIVE ADVERSARIAL NETS
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Size-Independent Sample Complexity of Neural Networks (2017) — 640 citations
- A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks (2018) — 350 citations
- Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems (2020) — 117 citations
- Pure Differentially Private Summation from Anonymous Messages (2020) — 51 citations
- Tight last-iterate convergence rates for no-regret learning in multi-player games (2020) — 97 citations
- Private Heavy Hitters and Range Queries in the Shuffled Model (2019) — 6 citations
- On the Power of Multiple Anonymous Messages (2019) — 112 citations