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Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning


Jun 07, 2021
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, 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


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Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding


Oct 27, 2020
Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato

* Accepted at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020) 

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Training independent subnetworks for robust prediction


Oct 13, 2020
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran


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Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters


Sep 30, 2018
Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato

* Under review as a conference paper at ICLR 2019 

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Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo


Jun 19, 2018
Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes


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Deep Gaussian Processes with Decoupled Inducing Inputs


Jan 09, 2018
Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes


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