Gabriel Huang
Artificial Intelligence · 2017–2021
A researcher in the Science corpus: 5 papers; trusts 317 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 104 citations into it; #15,463 of 35,345 by trust here, #13,760 by citations
- Computer Vision and Pattern Recognition — 103 citations into it; #9,888 of 21,575 by trust here, #9,435 by citations
- Generative Models — 50 citations into it
- Optimization & Training — 40 citations into it; #1,855 of 2,601 by trust here, #1,509 by citations
- Representation & Self-Supervised Learning — 34 citations into it
- Statistical and Nonlinear Physics — 14 citations into it; #5,811 of 10,127 by trust here, #3,284 by citations
- Signal Processing — 13 citations into it
- Safety, Robustness & Interpretability — 10 citations into it
Whom Gabriel Huang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Marco Cuturi
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Yoshua Bengio
- Yann LeCun
- Kaiming He
- Oriol Vinyals
- Ilya Sutskever
- Ruslan Salakhutdinov
Who trusts Gabriel Huang overall
- Kai Han
- Juho Lee
- Gauthier Gidel
- Micah Goldblum
- Tristan Sylvain
- Andrea Vedaldi
- Yoonho Lee
- Waïss Azizian
- Hugo Berard
- R. Devon Hjelm
Papers from Gabriel Huang's perspective
Ordered by how strongly the researchers Gabriel Huang 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
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Negative Momentum for Improved Game Dynamics (2018) — 192 citations
- Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification (2019) — 22 citations
- Scattering Networks for Hybrid Representation Learning (2018) — 99 citations
- Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning (2021) — 7 citations
- Parametric Adversarial Divergences are Good Task Losses for Generative Modeling (2017) — 13 citations