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PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning

Feb 24, 2021
Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar

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Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty

Feb 23, 2021
Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal

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Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression

Feb 22, 2021
Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, Yarin Gal

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Domain Invariant Representation Learning with Domain Density Transformations

Feb 14, 2021
A. Tuan Nguyen, Toan Tran, Yarin Gal, Atılım Güneş Baydin

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Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition

Feb 02, 2021
Panagiotis Tigas, Téo Bloch, Vishal Upendran, Banafsheh Ferdoushi, Mark C. M. Cheung, Siddha Ganju, Ryan M. McGranaghan, Yarin Gal, Asti Bhatt

* Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada 

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Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning

Feb 01, 2021
Luiz F. G. dos Santos, Souvik Bose, Valentina Salvatelli, Brad Neuberg, Mark C. M. Cheung, Miho Janvier, Meng Jin, Yarin Gal, Paul Boerner, Atılım Güneş Baydin

* 12 pages, 7 figures, 8 tables. This is a pre-print of an article submitted and accepted by A&A Journal 

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On Statistical Bias In Active Learning: How and When To Fix It

Jan 27, 2021
Sebastian Farquhar, Yarin Gal, Tom Rainforth

* Published at ICLR 2021 (Spotlight) 

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Technology Readiness Levels for Machine Learning Systems

Jan 11, 2021
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava Newman, Sujoy Ganguly, Danny Lange, Atılım Güneş Baydin, Amit Sharma, Adam Gibson, Yarin Gal, Eric P. Xing, Chris Mattmann, James Parr

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On Batch Normalisation for Approximate Bayesian Inference

Dec 24, 2020
Jishnu Mukhoti, Puneet K. Dokania, Philip H. S. Torr, Yarin Gal

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Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders

Nov 17, 2020
Mizu Nishikawa-Toomey, Lewis Smith, Yarin Gal

* Accepted at the workshop for Machine Learning and the Physical Sciences, 34th Conference on Neural Information Processing Systems (NeurIPS) December 11, 2020 

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On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes

Nov 01, 2020
Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth

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Inter-domain Deep Gaussian Processes

Nov 01, 2020
Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal

* Published in Proceedings of the 37th International Conference on Machine Learning (ICML 2020) 

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A Bayesian Perspective on Training Speed and Model Selection

Oct 27, 2020
Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk

* To be presented at NeurIPS 2020 

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Physics-informed GANs for Coastal Flood Visualization

Oct 16, 2020
Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz-Rodriguez, Océane Boulais, Aaron Piña, Dava Newman, Alexander Lavin, Yarin Gal, Chedy Raïssi

* Under Review 

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Interlocking Backpropagation: Improving depthwise model-parallelism

Oct 08, 2020
Aidan N. Gomez, Oscar Key, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal

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On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission

Jul 27, 2020
Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomáš Gavenčiak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal

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SliceOut: Training Transformers and CNNs faster while using less memory

Jul 21, 2020
Pascal Notin, Aidan N. Gomez, Joanna Yoo, Yarin Gal

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Single Shot Structured Pruning Before Training

Jul 01, 2020
Joost van Amersfoort, Milad Alizadeh, Sebastian Farquhar, Nicholas Lane, Yarin Gal

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Identifying Causal Effect Inference Failure with Uncertainty-Aware Models

Jul 01, 2020
Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal

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Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Jun 26, 2020
Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal

* Camera-ready version, International Conference of Machine Learning 2020 

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Learning Invariant Representations for Reinforcement Learning without Reconstruction

Jun 18, 2020
Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, Sergey Levine

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Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers

Jun 08, 2020
Tim Z. Xiao, Aidan N. Gomez, Yarin Gal

* 19 pages, 9 figures 

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Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search

Jun 08, 2020
Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal

* 14 pages, 10 figures 

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Uncertainty Evaluation Metric for Brain Tumour Segmentation

May 28, 2020
Raghav Mehta, Angelos Filos, Yarin Gal, Tal Arbel

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On the Benefits of Invariance in Neural Networks

May 01, 2020
Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy

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Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning

Apr 09, 2020
Andreas Kirsch, Clare Lyle, Yarin Gal

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Capsule Networks -- A Probabilistic Perspective

Apr 07, 2020
Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk

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