Siyuan Ma
Artificial Intelligence · 2017–2023
A researcher in the Science corpus: 8 papers; trusts 117 researchers and is trusted by 42 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 145 citations into it; #4,705 of 35,345 by trust here, #3,957 by citations
- Optimization & Training — 61 citations into it; #2,468 of 2,601 by trust here, #2,313 by citations
- Computer Vision and Pattern Recognition — 54 citations into it; #9,109 of 21,575 by trust here, #8,612 by citations
- Statistical Learning Theory — 37 citations into it
- Scaling & Learning Theory — 31 citations into it; #94 of 428 by trust here, #84 by citations
- Health Informatics — 30 citations into it
- Statistics and Probability — 30 citations into it; #11,875 of 12,932 by trust here, #9,233 by citations
- Computational Mechanics — 27 citations into it; #1,058 of 10,001 by trust here, #842 by citations
Whom Siyuan Ma trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Yann LeCun
- L. Eon Bottou
- Yoshua Bengio
- Geoffrey E. Hinton
- Mikhail Belkin
- Nicolas Le Roux
- Quoc V. Le
- Diederik P. Kingma
- Francis R. Bach
Who trusts Siyuan Ma overall
- Mikhail Belkin
- Chaoyue Liu
- Sharan Vaswani
- Soham De
- Sebastian U. Stich
- Raef Bassily
- Samuel L. Smith
- Vitaly Feldman
- Aditi Raghunathan
- Hilal Asi
Papers from Siyuan Ma's perspective
Ordered by how strongly the researchers Siyuan Ma 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
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Reconciling modern machine-learning practice and the classical bias–variance trade-off (2018) — 2,107 citations
- To understand deep learning we need to understand kernel learning (2018) — 446 citations
- The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning (2017) — 334 citations
- Diving into the shallows: a computational perspective on large-scale shallow learning (2017) — 81 citations
- On exponential convergence of SGD in non-convex over-parametrized learning (2018) — 112 citations
- Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models (2023) — 745 citations
- Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models (2023) — 263 citations
- Reconciling modern machine learning and the bias-variance trade-off (2018) — 136 citations