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Linh Tran (she/her), Ph.D.

Email | CV | Google Scholar | Twitter

I am a Senior Research Scientist at Autodesk AI lab. I also serve as a Board Member for the non-profit organization Women in Machine Learning (WiML). I completed my PhD at the Imperial College London under the supervision of Maja Pantic. Before joining Autodesk, I was a research intern at Google Brain and Samsung AI.

I am generally interested in learning with limited supervision, deep generative modeling, representation learning, and its application to 2D and 3D vision. My research focuses on learning interpretable and human-controllable representations using deep probabilistic modeling. In particular, I am interested in learning representations with limited supervision that generalizes to unseen environments and across tasks. I am excited by applying these methods to 2D and 3D computer vision, fairness, and robotics (vision-based, Sim-to-Real transfer) in which large-scale data is available, however, annotations are scarce. I am also interested in sequential data modeling, approximate inference, and model distillation.

Please contact me if you are interested to collaborate. The Autodesk AI lab has internship positions open every summer.

Outside of work, I enjoy travelling the world with family & friends, gardening on my balcony and trying numerous balancing yoga poses.

News

1 Paper accepted to PPSN 2022

Our paper “Evolving Through the Looking Glass: Learning Improved Search Spaces with Variational Autoencoders” was accepted to PPSN 2022. As short version of this paper was accepted to the Workshop on Enhancing Generative Machine Learning with Evolutionary Computation (EGML) at GECCO 2022.

Joining Women in Machine Learning (WiML) as Board Director

I am happy to announce to joining the Women in Machine Learning (WiML) Board of Directors Board of Directors. In particular, I will be working in the Finance Committee.

1 Paper accepted to CVPR 2022

Our paper “JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints” was accepted to CVPR 2022.

Neurips 2021 Outstanding Reviewer Award (8%)

I am very grateful (and proud!) to have received Outstanding Reviewer Award from NeurIPS this year.

Internship at the AI lab for 2022 (summer)

Our team (Autodesk AI lab) is looking for interns for the summer of 2022. You can apply following this link via myworkdayjobs. Feel free to reach out to me if you want to chat and know more!

Publications

Pre-prints

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Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness
S. Asgari, A. Gholami, F. Khani, K. Choi, L. Tran, R. Zhang and A. Khani
arXiv preprint arXiv:2207.01548 2022
[ arxiv ]

Conference articles

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Evolving Through the Looking Glass: Learning Improved Search Spaces with Variational Autoencoders
P. J. Bentley, S. L. Lim, A. Gaier and L. Tran
17th International Conference on Parallel Problem Solving from Nature (PPSN XVII), 2022
[ code ]
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JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints
K. D. D. Willis, P. K. Jayaraman, H. Chu, Y. Tian, Y. Li, D. Grandi, A. Sanghi, L. Tran, J. G. Lambourne, A. Solar-Lezama and W. Matusik
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[ arxiv | cvpr proceedings | poster | code | dataset ]
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How Good is the Bayes Posterior in Deep Neural Networks Really?
F. Wenzel*, K. Roth*, B.S. Veeling*, J. Swiatkowski, Tran, L., S. Mandt, J. Snoek, T. Salimans, R. Jenatton and S. Nowozin (* denotes equal contribution)
37th International Conference on Machine Learning (ICML), 2020
[ arxiv | icml proceedings | github ]
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The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
J. Swiatkowski, K. Roth, B.S. Veeling, L. Tran, J.V. Dillon, S. Mandt, J. Snoek, T. Salimans, R. Jenatton and S. Nowozin
37th International Conference on Machine Learning (ICML), 2020
[ arxiv | icml proceedings ]
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GAGAN: Geometry-Aware Generative Adversarial networks
J. Kossaifi, L. Tran, Y. Panagakis and M. Pantic
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[ arxiv | cvpr proceedings ]
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Deepcoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding.
D. L. Tran*, R. Walecki*, Ognjen (Oggi) Rudovic*, S. Eleftheriadis, B. Schüller and M. Pantic (* denotes equal contribution)
Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017
[ arxiv | iccv proceedings | poster ]

Journal articles

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Cauchy-Schwarz Regularized Autoencoder
L. Tran, M. Pantic and M.P. Deisenroth
Journal of Machine Learning Research, 2022
[ arxiv | jmlr proceedings | code ]
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Disentangling Geometry and Appearance with Regularised Geometry-Aware Generative Adversarial Networks
L. Tran, J. Kossaifi, Y. Panagakis and M. Pantic
International Journal of Computer Vision, pp.1-21, 2019.
[ ijcv open access ]
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ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes
L. Tran, T. Hamp and B. Rost
Plos ONE, 2018
[ bioarxiv | plos one open access | project page ]

Peer-reviewed workshop articles

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COIL: Constrained Optimization in Learned Latent Space
P. J. Bentley, S. L. Lim, A. Gaier and L. Tran
Workshop on Enhancing Generative Machine Learning with Evolutionary Computation (EGML), GECCO 2022
[ arxiv (long version) | workshop page | workshop paper | code ]
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Weakly-Supervised Group Disentanglement using Total Correlation
L. Tran, S.A. Taghanaki, A.H. Khasahmadi and A. Sanghi
Workshop on Weakly Supervised Learning (WeaSuL), ICLR 2021
[ arxiv (long version) | workshop page | workshop paper ]
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Hydra: Preserving Ensemble Diversity for Model Distillation
L. Tran, B.S. Veeling, K. Roth, J. Swiatkowski, J.V. Dillon, J. Snoek, S. Mandt, T. Salimans, S. Nowozin and R. Jenatton
Uncertainty & Robustness in Deep Learning (UDL), ICML 2020
[ arxiv (long version) | workshop page | workshop paper ]

Other

Human-Controllable and Structured Deep Generative Models
L. Tran
Ph.D. Thesis
[ pdf ]