Junbo Jake Zhao
Artificial Intelligence · 2015–2020
A researcher in the Science corpus: 7 papers; trusts 411 researchers and is trusted by 2,172 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 119 citations into it; #378 of 35,345 by trust here, #1,560 by citations
- Computer Vision and Pattern Recognition — 113 citations into it; #1,127 of 21,575 by trust here, #1,683 by citations
- Generative Models — 69 citations into it; #155 of 1,956 by trust here, #240 by citations
- Representation & Self-Supervised Learning — 44 citations into it; #1,975 of 3,145 by trust here, #1,181 by citations
- Natural Language Processing — 39 citations into it; #156 of 5,688 by trust here, #455 by citations
- Model Architectures — 32 citations into it
- Signal Processing — 20 citations into it; #3,959 of 7,572 by trust here, #1,504 by citations
- Optimization & Training — 18 citations into it
Whom Junbo Jake Zhao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yann LeCun
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Marc'Aurelio Ranzato
- L. Eon Bottou
- Tomas Mikolov
- Kaiming He
Who trusts Junbo Jake Zhao overall
- Yoshua Bengio
- Chunyuan Li
- Kyunghyun Cho
- Yonatan Belinkov
- Timo Schick
- Quoc V. Le
- Armand Joulin
- Ruslan Salakhutdinov
- Yi Tay
- Dinghan Shen
Papers from Junbo Jake Zhao's perspective
Ordered by how strongly the researchers Junbo Jake Zhao 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)
- 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Energy-based Generative Adversarial Network (2016) — 1,148 citations
- Character-level Convolutional Networks for Text Classification (2015) — 7,191 citations
- Stacked What-Where Auto-encoders (2015) — 263 citations
- Disentangling factors of variation in deep representation using adversarial training (2016) — 521 citations
- Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs (2019) — 884 citations
- PIANOTREE VAE: Structured Representation Learning for Polyphonic Music (2020) — 66 citations
- Adversarially Regularized Autoencoders (2017) — 300 citations