He Ma
Computer Vision and Pattern Recognition · 2016–2018
A researcher in the Science corpus: 2 papers; trusts 84 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 39 citations into it; #11,849 of 21,575 by trust here, #12,419 by citations
- Generative Models — 37 citations into it; #1,087 of 1,956 by trust here, #1,355 by citations
- Artificial Intelligence — 24 citations into it
- Probabilistic & Bayesian Methods — 11 citations into it
- Statistical and Nonlinear Physics — 10 citations into it
- Signal Processing — 8 citations into it
- Model Architectures — 7 citations into it
- Evaluation & Benchmarks — 6 citations into it; #1,941 of 3,147 by trust here, #2,393 by citations
Whom He Ma trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Ruslan Salakhutdinov
- Yann LeCun
- Ian J. Goodfellow
- Alex Krizhevsky
- Diederik P. Kingma
- Daniel Jiwoong Im
- Alec Radford
- Radford M. Neal
Who trusts He Ma overall
- Timothée Lesort
- Ryan Webster
- Loïc Simon
- Tal Ben-Nun
- Anne Marie Delaney
- Yibo Li
- Daniel Jiwoong Im
- Shaohui Liu
- Tristan Sylvain
- Mehdi S. M. Sajjadi
Papers from He Ma's perspective
Ordered by how strongly the researchers He Ma trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- 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
- GENERATIVE ADVERSARIAL NETS
Their papers
- Quantitatively Evaluating GANs With Divergences Proposed for Training (2018) — 73 citations
- Generative Adversarial Parallelization (2016) — 40 citations