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Ricky Tian Qi Chen
Ricky Tian Qi Chen
Research Scientist, FAIR
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Title
Cited by
Cited by
Year
Neural ordinary differential equations
RTQ Chen, Y Rubanova, J Bettencourt, DK Duvenaud
Advances in neural information processing systems, 6571-6583, 2018
22162018
Isolating Sources of Disentanglement in Variational Autoencoders
RTQ Chen, X Li, R Grosse, D Duvenaud
Advances in Neural Information Processing Systems, NIPS 2018, 2018
7852018
FFJORD: Free-form continuous dynamics for scalable reversible generative models
W Grathwohl, RTQ Chen, J Betterncourt, I Sutskever, D Duvenaud
International Conference on Learning Representations, ICLR 2019, 2019
4542019
Latent odes for irregularly-sampled time series
Y Rubanova, RTQ Chen, D Duvenaud
Advances in Neural Information Processing Systems, NeurIPS 2019, 2019
359*2019
Invertible residual networks
J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen
International Conference on Machine Learning, ICML 2019, 2019
3472019
Fast patch-based style transfer of arbitrary style
RTQ Chen, M Schmidt
Constructive Machine Learning Workshop, NIPS 2016, 2016
2922016
Residual flows for invertible generative modeling
RTQ Chen, J Behrmann, DK Duvenaud, JH Jacobsen
Advances in Neural Information Processing Systems, 9913-9923, 2019
1952019
Scalable gradients for stochastic differential equations
X Li, TKL Wong, RTQ Chen, D Duvenaud
International Conference on Artificial Intelligence and Statistics, 3870-3882, 2020
1432020
Scalable reversible generative models with free-form continuous dynamics
W Grathwohl, RTQ Chen, J Bettencourt, D Duvenaud
International Conference on Learning Representations, 2019
562019
Learning Neural Event Functions for Ordinary Differential Equations
RTQ Chen, B Amos, M Nickel
International Conference on Learning Representations, ICLR 2021, 2021
382021
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
CW Huang, RTQ Chen, C Tsirigotis, A Courville
International Conference on Learning Representations, ICLR 2021, 2021
332021
Neural Spatio-Temporal Point Processes
RTQ Chen, B Amos, M Nickel
International Conference on Learning Representations, ICLR 2021, 2021
252021
Neural networks with cheap differential operators
RTQ Chen, D Duvenaud
Advances in Neural Information Processing Systems, 9961-9971, 2019
212019
“Hey, that’s not an ODE”: Faster ODE Adjoints via Seminorms
P Kidger, RTQ Chen, T Lyons
International Conference on Machine Learning, ICML 2021, 2021
16*2021
SUMO: Unbiased estimation of log marginal probability for latent variable models
Y Luo, A Beatson, M Norouzi, J Zhu, D Duvenaud, RP Adams, RTQ Chen
International Conference on Learning Representations, ICLR 2020, 2020
162020
Scalable gradients and variational inference for stochastic differential equations
X Li, TKL Wong, RTQ Chen, DK Duvenaud
Symposium on Advances in Approximate Bayesian Inference, 1-28, 2020
152020
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations
W Xu, RTQ Chen, X Li, D Duvenaud
Artificial Intelligence and Statistics, AISTATS 2022, 2022
102022
torchdiffeq, 2018
RTQ Chen
URl: https://github. com/rtqichen/torchdiffeq, 0
10
Fully differentiable optimization protocols for non-equilibrium steady states
R Vargas, RTQ Chen, KA Jung, P Brumer
New Journal of Physics, 2021
42021
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering
RTQ Chen, D Choi, L Balles, D Duvenaud, P Hennig
Workshop on "I Can't Believe It's Not Better!", NeurIPS 2020, 2020
42020
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