More from the Same Authors

• 2021 : Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks »
  Neil Band · Tim G. J. Rudner · Qixuan Feng · Angelos Filos · Zachary Nado · Mike Dusenberry · Ghassen Jerfel · Dustin Tran · Yarin Gal
• 2021 : Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks »
  Neil Band · Tim G. J. Rudner · Qixuan Feng · Angelos Filos · Zachary Nado · Mike Dusenberry · Ghassen Jerfel · Dustin Tran · Yarin Gal
• 2021 : Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning »
  Zachary Nado · Neil Band · Mark Collier · Josip Djolonga · Mike Dusenberry · Sebastian Farquhar · Qixuan Feng · Angelos Filos · Marton Havasi · Rodolphe Jenatton · Ghassen Jerfel · Jeremiah Liu · Zelda Mariet · Jeremy Nixon · Shreyas Padhy · Jie Ren · Tim G. J. Rudner · Yeming Wen · Florian Wenzel · Kevin Murphy · D. Sculley · Balaji Lakshminarayanan · Jasper Snoek · Yarin Gal · Dustin Tran
• 2021 : Deep Classifiers with Label Noise Modeling and Distance Awareness »
  Vincent Fortuin · Mark Collier · Florian Wenzel · James Allingham · Jeremiah Liu · Dustin Tran · Balaji Lakshminarayanan · Jesse Berent · Rodolphe Jenatton · Effrosyni Kokiopoulou
• 2021 : Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks »
  Neil Band · Tim G. J. Rudner · Qixuan Feng · Angelos Filos · Zachary Nado · Mike Dusenberry · Ghassen Jerfel · Dustin Tran · Yarin Gal
• 2022 : Reliability benchmarks for image segmentation »
  Estefany Kelly Buchanan · Michael Dusenberry · Jie Ren · Kevin Murphy · Balaji Lakshminarayanan · Dustin Tran
• 2021 : Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks »
  Neil Band · Tim G. J. Rudner · Qixuan Feng · Angelos Filos · Zachary Nado · Mike Dusenberry · Ghassen Jerfel · Dustin Tran · Yarin Gal
• 2021 Poster: Soft Calibration Objectives for Neural Networks »
  Archit Karandikar · Nicholas Cain · Dustin Tran · Balaji Lakshminarayanan · Jonathon Shlens · Michael Mozer · Becca Roelofs
• 2021 Poster: Revisiting the Calibration of Modern Neural Networks »
  Matthias Minderer · Josip Djolonga · Rob Romijnders · Frances Hubis · Xiaohua Zhai · Neil Houlsby · Dustin Tran · Mario Lucic
• 2020 Session: Orals & Spotlights Track 33: Health/AutoML/(Soft|Hard)ware »
  Dustin Tran · Artur Dubrawski
• 2020 Poster: Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness »
  Jeremiah Liu · Zi Lin · Shreyas Padhy · Dustin Tran · Tania Bedrax Weiss · Balaji Lakshminarayanan
• 2020 Poster: Hyperparameter Ensembles for Robustness and Uncertainty Quantification »
  Florian Wenzel · Jasper Snoek · Dustin Tran · Rodolphe Jenatton
• 2020 Tutorial: (Track2) Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning Q&A »
  Dustin Tran · Balaji Lakshminarayanan · Jasper Snoek
• 2020 Tutorial: (Track2) Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning »
  Dustin Tran · Balaji Lakshminarayanan · Jasper Snoek
• 2019 Poster: Bayesian Layers: A Module for Neural Network Uncertainty »
  Dustin Tran · Mike Dusenberry · Mark van der Wilk · Danijar Hafner
• 2019 Poster: Discrete Flows: Invertible Generative Models of Discrete Data »
  Dustin Tran · Keyon Vafa · Kumar Agrawal · Laurent Dinh · Ben Poole
• 2018 : Software Panel »
  Ben Letham · David Duvenaud · Dustin Tran · Aki Vehtari
• 2018 Poster: Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language »
  Matthew D. Hoffman · Matthew Johnson · Dustin Tran
• 2018 Poster: Simple, Distributed, and Accelerated Probabilistic Programming »
  Dustin Tran · Matthew Hoffman · Dave Moore · Christopher Suter · Srinivas Vasudevan · Alexey Radul · Matthew Johnson · Rif A. Saurous
• 2018 Poster: Mesh-TensorFlow: Deep Learning for Supercomputers »
  Noam Shazeer · Youlong Cheng · Niki Parmar · Dustin Tran · Ashish Vaswani · Penporn Koanantakool · Peter Hawkins · HyoukJoong Lee · Mingsheng Hong · Cliff Young · Ryan Sepassi · Blake Hechtman
• 2017 : Deep Probabilistic Programming »
  Dustin Tran
• 2017 : Contributed talk 3: Implicit Causal Models for Genome-wide Association Studies »
  Dustin Tran
• 2017 : Introduction »
  Cheng Zhang · Francisco Ruiz · Dustin Tran · James McInerney · Stephan Mandt
• 2017 Workshop: Advances in Approximate Bayesian Inference »
  Francisco Ruiz · Stephan Mandt · Cheng Zhang · James McInerney · James McInerney · Dustin Tran · Dustin Tran · David Blei · Max Welling · Tamara Broderick · Michalis Titsias
• 2017 Poster: Hierarchical Implicit Models and Likelihood-Free Variational Inference »
  Dustin Tran · Rajesh Ranganath · David Blei
• 2017 Poster: Variational Inference via $\chi$ Upper Bound Minimization »
  Adji Bousso Dieng · Dustin Tran · Rajesh Ranganath · John Paisley · David Blei
• 2016 Workshop: Advances in Approximate Bayesian Inference »
  Tamara Broderick · Stephan Mandt · James McInerney · Dustin Tran · David Blei · Kevin Murphy · Andrew Gelman · Michael I Jordan
• 2016 Poster: Operator Variational Inference »
  Rajesh Ranganath · Dustin Tran · Jaan Altosaar · David Blei
• 2015 : Variational Gaussian Process »
  Dustin Tran
• 2015 Workshop: Advances in Approximate Bayesian Inference »
  Dustin Tran · Tamara Broderick · Stephan Mandt · James McInerney · Shakir Mohamed · Alp Kucukelbir · Matthew D. Hoffman · Neil Lawrence · David Blei
• 2015 Poster: Copula variational inference »
  Dustin Tran · David Blei · Edo M Airoldi