Fei Ye

Artificial Intelligence · with Bors · 2019–2024

A researcher in the Science corpus: 16 papers; trusts 1,709 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 344 citations into it; #10,398 of 35,345 by trust here, #9,641 by citations
• Computer Vision and Pattern Recognition — 343 citations into it; #7,959 of 21,575 by trust here, #6,542 by citations
• Generative Models — 190 citations into it; #1,222 of 1,956 by trust here, #1,502 by citations
• Representation & Self-Supervised Learning — 137 citations into it; #2,142 of 3,145 by trust here, #2,404 by citations
• Meta & Continual Learning — 118 citations into it; #789 of 1,041 by trust here, #1,014 by citations
• Probabilistic & Bayesian Methods — 67 citations into it
• Optimization & Training — 59 citations into it
• Statistical and Nonlinear Physics — 40 citations into it; #4,946 of 10,127 by trust here, #6,727 by citations

Whom Fei Ye trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yann LeCun
5. Yoshua Bengio
6. Kaiming He
7. Max Welling
8. Ilya Sutskever
9. L. Eon Bottou
10. Adrian G. Bors

Who trusts Fei Ye overall

1. Adrian G. Bors
2. Byeonghwi Kim
3. Haoxuan Qu
4. Liyuan Wang
5. Minhyuk Seo
6. Hossein Rahmani
7. Senthil Purushwalkam
8. Rui Gao
9. Jonghyun Choi
10. Anne Marie Delaney

Papers from Fei Ye's perspective

Ordered by how strongly the researchers Fei Ye trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network (2019) — 350 citations
• Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster Memory (2024) — 14 citations
• Continual Variational Autoencoder Learning via Online Cooperative Memorization (2022) — 23 citations
• Learning an Evolved Mixture Model for Task-Free Continual Learning (2022) — 10 citations
• Task-Free Continual Learning via Online Discrepancy Distance Learning (2022) — 38 citations
• Lifelong Generative Modelling Using Dynamic Expansion Graph Model (2021) — 14 citations
• Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process (2021) — 26 citations
• Deep Mixture Generative Autoencoders (2021) — 37 citations
• Lifelong Teacher-Student Network Learning (2021) — 57 citations
• Lifelong Mixture of Variational Autoencoders (2021) — 36 citations
• Learning joint latent representations based on information maximization (2021) — 29 citations
• InfoVAEGAN: Learning Joint Interpretable Representations by Information Maximization and Maximum Likelihood (2021) — 16 citations
• Lifelong Twin Generative Adversarial Networks (2021) — 14 citations
• Mixtures of Variational Autoencoders (2020) — 14 citations
• Lifelong learning of interpretable image representations (2020) — 13 citations
• Learning latent representations across multiple data domains using Lifelong VAEGAN (2020) — 83 citations