Jerry Ma
Artificial Intelligence · 2014–2020
A researcher in the Science corpus: 6 papers; trusts 298 researchers and is trusted by 28 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 95 citations into it; #7,168 of 35,345 by trust here, #16,471 by citations
- Molecular Biology — 95 citations into it; #16,538 of 17,773 by trust here, #4,132 by citations
- Computational Biology & Bioinformatics — 91 citations into it
- Optimization & Training — 46 citations into it; #755 of 2,601 by trust here, #1,804 by citations
- Computer Vision and Pattern Recognition — 43 citations into it; #6,095 of 21,575 by trust here, #15,529 by citations
- Natural Language Processing — 36 citations into it
- Model Architectures — 33 citations into it
- Materials Chemistry — 28 citations into it
Whom Jerry Ma trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Diederik P. Kingma
- Kaiming He
- Geoffrey E. Hinton
- Yann LeCun
- Boris Polyak
- Yoshua Bengio
- Ilya Sutskever
- Jimmy Ba
- Yurii E. Nesterov
Who trusts Jerry Ma overall
- Mahmoud Assran
- Yanli Liu
- Anas Barakat
- Amanpreet Singh
- Valentina Zantedeschi
- Sergei Popov
- Ding Wang
- Jianyu Wang
- Pedro H. P. Savarese
- Igor Gitman
Papers from Jerry Ma's perspective
Ordered by how strongly the researchers Jerry 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Quasi-hyperbolic momentum and Adam for deep learning (2018) — 158 citations
- ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero (2019) — 120 citations
- On the adequacy of untuned warmup for adaptive optimization (2019) — 83 citations
- Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019) — 3,265 citations
- Energy-based models for atomic-resolution protein conformations (2020) — 61 citations
- A Study of Probabilistic Password Models (2014) — 315 citations