Ricky T. Q. Chen
Artificial Intelligence · 2018–2024
A researcher in the Science corpus: 10 papers; trusts 371 researchers and is trusted by 61 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 141 citations into it; #5,736 of 35,345 by trust here, #5,880 by citations
- Computer Vision and Pattern Recognition — 141 citations into it; #4,454 of 21,575 by trust here, #3,404 by citations
- Generative Models — 100 citations into it; #406 of 1,956 by trust here, #386 by citations
- Statistical and Nonlinear Physics — 76 citations into it; #1,056 of 10,127 by trust here, #484 by citations
- Probabilistic & Bayesian Methods — 47 citations into it
- Model Architectures — 41 citations into it
- Signal Processing — 33 citations into it; #3,044 of 7,572 by trust here, #812 by citations
- Optimization & Training — 29 citations into it; #2,404 of 2,601 by trust here, #1,904 by citations
Whom Ricky T. Q. Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Laurent Dinh
- Alex Krizhevsky
- Yoshua Bengio
- Max Welling
- Danilo Jimenez Rezende
- Ilya Sutskever
- Yann LeCun
- Kaiming He
Who trusts Ricky T. Q. Chen overall
- Chin-Wei Huang
- Yang Song
- Polina Kirichenko
- Jonas Köhler
- Arash Vahdat
- Jianfei Chen
- Cheng Lu
- Pavel Izmailov
- R. Cornish
- Hao Liu
Papers from Ricky T. Q. Chen's perspective
Ordered by how strongly the researchers Ricky T. Q. 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
- Gradient-based learning applied to document recognition
- 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)
- GENERATIVE ADVERSARIAL NETS
Their papers
- FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models (2018) — 1,080 citations
- Flow Matching for Generative Modeling (2022) — 5,473 citations
- Residual Flows for Invertible Generative Modeling (2019) — 450 citations
- Matching Normalizing Flows and Probability Paths on Manifolds (2022) — 64 citations
- Neural Networks with Cheap Differential Operators (2019) — 39 citations
- Latent ODEs for Irregularly-Sampled Time Series (2019) — 340 citations
- Discrete Flow Matching (2024) — 313 citations
- Flow Matching Guide and Code (2024) — 298 citations
- Bespoke Solvers for Generative Flow Models (2023) — 43 citations
- Scalable Gradients for Stochastic Differential Equations (2020) — 434 citations