Dustin Tran
I am a research scientist at Google DeepMind. I am broadly interested in intelligence under themes like programs and probability.
URL: dustintran.com
Research Areas
Authored Publications
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Scaling Vision Transformers to 22 Billion Parameters
Josip Djolonga
Basil Mustafa
Piotr Padlewski
Justin Gilmer
Mathilde Caron
Rodolphe Jenatton
Lucas Beyer
Michael Tschannen
Anurag Arnab
Carlos Riquelme
Matthias Minderer
Gamaleldin Elsayed
Fisher Yu
Avital Oliver
Fantine Huot
Mark Collier
Vighnesh Birodkar
Yi Tay
Alexander Kolesnikov
Filip Pavetić
Thomas Kipf
Xiaohua Zhai
Neil Houlsby
Arxiv (2023)
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Shreyas Padhy
Zi Lin
Yeming Wen
Ghassen Jerfel
Journal of Machine Learning Research (2022)
Plex: Towards Reliability using Pretrained Large Model Extensions
Du Phan
Mark Patrick Collier
Zi Wang
Zelda Mariet
Clara Huiyi Hu
Neil Band
Tim G. J. Rudner
Karan Singhal
Joost van Amersfoort
Andreas Christian Kirsch
Rodolphe Jenatton
Honglin Yuan
Kelly Buchanan
D. Sculley
Yarin Gal
ICML 2022 Pre-training Workshop (2022)
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
Martin Mladenov
Vihan Jain
Christopher Colby
Nicolas Mayoraz
Hubert Pham
Ivan Vendrov
ArXiv (2021)
Combining Ensembles and Data Augmentation Can Harm Your Calibration
Yeming Wen
Ghassen Jerfel
Rafael Rios Müller
International Conference on Learning Representations (2021)
Revisiting the Calibration of Modern Neural Networks
Matthias Minderer
Josip Djolonga
Rob Romijnders
Frances Ann Hubis
Xiaohua Zhai
Neil Houlsby
Neural Information Processing Systems (2021) (to appear)
Training independent subnetworks for robust prediction
Marton Havasi
Rodolphe Jenatton
Stanislav Fort
International Conference on Learning Representations (2021)
Soft Calibration Objectives for Neural Networks
Archit Karandikar
Nick Cain
Jon Shlens
Michael C. Mozer
Becca Roelofs
Advances in Neural Information Processing Systems (NeurIPS) (2021)
Deep Classifiers with Label Noise Modeling and Distance Awareness
Vincent Fortuin
Mark Patrick Collier
Florian Wenzel
James Urquhart Allingham
Jesse Berent
Rodolphe Jenatton
NeurIPS 2021 Workshop on Bayesian Deep Learning (2021) (to appear)
Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG
Martin Mladenov
Vihan Jain
Christopher Colby
Nicolas Mayoraz
Hubert Pham
Ivan Vendrov
RecSys '20: Fourteenth ACM Conference on Recommender Systems (2020), pp. 591-593
