Dustin Tran - RankMe

81 papers A* 18A 4Journal 47Unranked 12

Year	Rank	Type	Title / Venue / Authors
2024	A*	`conf`	Long-form factuality in large language models. NeurIPS Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Jie Huang, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, Quoc V. Le
2024	J	`jnl`	Long-form factuality in large language models. CoRR Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, Quoc V. Le
2023	J	`jnl`	A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. J. Mach. Learn. Res. Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan
2023	A*	`conf`	A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. ICML James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan
2023	J	`jnl`	A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. CoRR James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Jeremiah Zhe Liu, Xiuye Gu, Yin Cui, Dustin Tran, Balaji Lakshminarayanan
2023	J	`jnl`	Larger language models do in-context learning differently. CoRR Jerry W. Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, Tengyu Ma
2023	A*	`conf`	Scaling Vision Transformers to 22 Billion Parameters. ICML Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, Neil Houlsby
2023	J	`jnl`	Scaling Vision Transformers to 22 Billion Parameters. CoRR Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, Neil Houlsby
2022	J	`jnl`	A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. CoRR Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan
2022	J	`jnl`	Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. CoRR Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal
2022	J	`jnl`	Deep Classifiers with Label Noise Modeling and Distance Awareness. Trans. Mach. Learn. Res. Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
2022	J	`jnl`	Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR Dustin Tran, Jeremiah Z. Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan
2022	J	`jnl`	Sparse MoEs meet Efficient Ensembles. Trans. Mach. Learn. Res. James Urquhart Allingham, Florian Wenzel, Zelda E. Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton
2021	—	`conf`	Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. NeurIPS Datasets and Benchmarks Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Mike Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal
2021	A*	`conf`	Combining Ensembles and Data Augmentation Can Harm Your Calibration. ICLR Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran
2021	J	`jnl`	Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Z. Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
2021	J	`jnl`	RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems. CoRR Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier
2021	A*	`conf`	Revisiting the Calibration of Modern Neural Networks. NeurIPS Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic
2021	J	`jnl`	Revisiting the Calibration of Modern Neural Networks. CoRR Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic
2021	J	`jnl`	Sampling the Variational Posterior with Local Refinement. Entropy Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon, José Miguel Hernández-Lobato
2021	A*	`conf`	Soft Calibration Objectives for Neural Networks. NeurIPS Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs
2021	J	`jnl`	Soft Calibration Objectives for Neural Networks. CoRR Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs
2021	J	`jnl`	Sparse MoEs meet Efficient Ensembles. CoRR James Urquhart Allingham, Florian Wenzel, Zelda E. Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton
2021	A*	`conf`	Training independent subnetworks for robust prediction. ICLR Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran
2021	J	`jnl`	Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. 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
2020	—	`conf`	Analyzing the role of model uncertainty for electronic health records. CHIL Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai
2020	J	`jnl`	BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning. CoRR Yeming Wen, Dustin Tran, Jimmy Ba
2020	A*	`conf`	BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning. ICLR Yeming Wen, Dustin Tran, Jimmy Ba
2020	J	`jnl`	Combining Ensembles and Data Augmentation can Harm your Calibration. CoRR Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran
2020	A	`conf`	Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG. RecSys Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier
2020	A*	`conf`	Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. ICML Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran
2020	J	`jnl`	Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. CoRR Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran
2020	J	`jnl`	Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data. J. Mach. Learn. Res. Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert
2020	A*	`conf`	Hyperparameter Ensembles for Robustness and Uncertainty Quantification. NeurIPS Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton
2020	J	`jnl`	Hyperparameter Ensembles for Robustness and Uncertainty Quantification. CoRR Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton
2020	—	`conf`	On the Discrepancy between Density Estimation and Sequence Generation. SPNLP@EMNLP Jason Lee, Dustin Tran, Orhan Firat, Kyunghyun Cho
2020	J	`jnl`	On the Discrepancy between Density Estimation and Sequence Generation. CoRR Jason Lee, Dustin Tran, Orhan Firat, Kyunghyun Cho
2020	A*	`conf`	Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. NeurIPS Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
2020	J	`jnl`	Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. CoRR Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
2020	J	`jnl`	Training independent subnetworks for robust prediction. CoRR Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran
2019	J	`jnl`	Analyzing the Role of Model Uncertainty for Electronic Health Records. CoRR Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai
2019	A*	`conf`	Bayesian Layers: A Module for Neural Network Uncertainty. NeurIPS Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner
2019	—	`conf`	Discrete Flows: Invertible Generative Models of Discrete Data. DGS@ICLR Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
2019	A*	`conf`	Discrete Flows: Invertible Generative Models of Discrete Data. NeurIPS Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
2019	J	`jnl`	Discrete Flows: Invertible Generative Models of Discrete Data. CoRR Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
2019	—	`conf`	Measuring Calibration in Deep Learning. CVPR Workshops Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran
2019	J	`jnl`	Measuring Calibration in Deep Learning. CoRR Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran
2019	A	`conf`	Noise Contrastive Priors for Functional Uncertainty. UAI Danijar Hafner, Dustin Tran, Timothy P. Lillicrap, Alex Irpan, James Davidson
2018	J	`jnl`	Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language. CoRR Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran
2018	J	`jnl`	Bayesian Layers: A Module for Neural Network Uncertainty. CoRR Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner
2018	—	`conf`	Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. ICLR (Poster) Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse
2018	J	`jnl`	Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. CoRR Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse
2018	A*	`conf`	Image Transformer. ICML Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran
2018	—	`conf`	Implicit Causal Models for Genome-wide Association Studies. ICLR (Poster) Dustin Tran, David M. Blei
2018	A*	`conf`	Mesh-TensorFlow: Deep Learning for Supercomputers. NeurIPS Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake A. Hechtman
2018	J	`jnl`	Mesh-TensorFlow: Deep Learning for Supercomputers. CoRR Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake A. Hechtman
2018	J	`jnl`	Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors. CoRR Danijar Hafner, Dustin Tran, Alex Irpan, Timothy P. Lillicrap, James Davidson
2018	A*	`conf`	Simple, Distributed, and Accelerated Probabilistic Programming. NeurIPS Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul
2018	J	`jnl`	Simple, Distributed, and Accelerated Probabilistic Programming. CoRR Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew J. Johnson, Rif A. Saurous
2017	J	`jnl`	Automatic Differentiation Variational Inference. J. Mach. Learn. Res. Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
2017	—	`conf`	Deep Probabilistic Programming. ICLR (Poster) Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
2017	J	`jnl`	Deep Probabilistic Programming. CoRR Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
2017	J	`jnl`	Deep and Hierarchical Implicit Models. CoRR Dustin Tran, Rajesh Ranganath, David M. Blei
2017	—	`conf`	Hierarchical Implicit Models and Likelihood-Free Variational Inference. NIPS Dustin Tran, Rajesh Ranganath, David M. Blei
2017	J	`jnl`	Implicit Causal Models for Genome-wide Association Studies. CoRR Dustin Tran, David M. Blei
2017	J	`jnl`	TensorFlow Distributions. CoRR Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matthew D. Hoffman, Rif A. Saurous
2017	—	`conf`	Variational Inference via \chi Upper Bound Minimization. NIPS Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei
2016	J	`jnl`	Automatic Differentiation Variational Inference. CoRR Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
2016	J	`jnl`	Edward: A library for probabilistic modeling, inference, and criticism. CoRR Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei
2016	A*	`conf`	Hierarchical Variational Models. ICML Rajesh Ranganath, Dustin Tran, David M. Blei
2016	—	`conf`	Operator Variational Inference. NIPS Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei
2016	J	`jnl`	Operator Variational Inference. CoRR Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei
2016	A	`conf`	Spectral M-estimation with Applications to Hidden Markov Models. AISTATS Dustin Tran, Minjae Kim, Finale Doshi-Velez
2016	J	`jnl`	Spectral M-estimation with Applications to Hidden Markov Models. CoRR Dustin Tran, Minjae Kim, Finale Doshi-Velez
2016	J	`jnl`	The $χ$-Divergence for Approximate Inference. CoRR Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei
2016	A	`conf`	Towards Stability and Optimality in Stochastic Gradient Descent. AISTATS Panos Toulis, Dustin Tran, Edoardo M. Airoldi
2016	A*	`conf`	Variational Gaussian Process. ICLR Dustin Tran, Rajesh Ranganath, David M. Blei
2015	—	`conf`	Copula variational inference. NIPS Dustin Tran, David M. Blei, Edoardo M. Airoldi
2015	J	`jnl`	Hierarchical Variational Models. CoRR Rajesh Ranganath, Dustin Tran, David M. Blei
2015	J	`jnl`	Stability and optimality in stochastic gradient descent. CoRR Panos Toulis, Dustin Tran, Edoardo M. Airoldi
2015	J	`jnl`	Variational inference with copula augmentation. CoRR Dustin Tran, David M. Blei, Edoardo M. Airoldi