Jianfei Chen
Artificial Intelligence · with Zhu · 2015–2023
A researcher in the Science corpus: 12 papers; trusts 491 researchers and is trusted by 20 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 232 citations into it; #25,670 of 35,345 by trust here, #6,614 by citations
- Computer Vision and Pattern Recognition — 209 citations into it; #10,902 of 21,575 by trust here, #3,955 by citations
- Generative Models — 126 citations into it; #1,249 of 1,956 by trust here, #599 by citations
- Probabilistic & Bayesian Methods — 78 citations into it
- Statistical and Nonlinear Physics — 64 citations into it
- Efficiency & Compression — 47 citations into it; #1,093 of 1,264 by trust here, #1,250 by citations
- Signal Processing — 46 citations into it; #6,497 of 7,572 by trust here, #4,900 by citations
- Model Architectures — 34 citations into it
Whom Jianfei Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Alex Krizhevsky
- Geoffrey E. Hinton
- Max Welling
- Kaiming He
- Yoshua Bengio
- Jimmy Ba
- Radford M. Neal
- Thomas Kipf
- Yann LeCun
Who trusts Jianfei Chen overall
- Arash Vahdat
- Ziniu Hu
- Christopher Morris
- Lukas Galke Poech
- Chen Wang
- Yinpeng Dong
- William L. Hamilton
- Jun Zhu
- Renjie Liao
- Ryan L. Murphy
Papers from Jianfei Chen's perspective
Ordered by how strongly the researchers Jianfei Chen 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
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- 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
- DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps (2022) — 2,469 citations
- DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models (2022) — 1,074 citations
- ZhuSuan: A Library for Bayesian Deep Learning (2017) — 43 citations
- VFlow: More Expressive Generative Flows with Variational Data Augmentation (2020) — 65 citations
- Parameter-efficient fine-tuning of large-scale pre-trained language models (2023) — 1,192 citations
- Fast Lossless Neural Compression with Integer-Only Discrete Flows (2022) — 8 citations
- Implicit Normalizing Flows (2021) — 38 citations
- A Statistical Framework for Low-bitwidth Training of Deep Neural Networks (2020) — 73 citations
- Stochastic Training of Graph Convolutional Networks (2017) — 32 citations
- Stochastic Training of Graph Convolutional Networks with Variance Reduction (2017) — 566 citations
- WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation (2015) — 74 citations
- WarpLDA: a Simple and Efficient O(1) Algorithm for Latent Dirichlet Allocation (2015) — 10 citations