Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Yarin Gal

Yarin Gal

DARTS without a Validation Set: Optimizing the Marginal Likelihood


Dec 24, 2021
Miroslav Fil, Binxin Ru, Clare Lyle, Yarin Gal

Figure 1 for DARTS without a Validation Set: Optimizing the Marginal Likelihood
Figure 2 for DARTS without a Validation Set: Optimizing the Marginal Likelihood
Figure 3 for DARTS without a Validation Set: Optimizing the Marginal Likelihood
Figure 4 for DARTS without a Validation Set: Optimizing the Marginal Likelihood
* Presented at the 5th Workshop on Meta-Learning at NeurIPS 2021 
  Access Paper or Ask Questions

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results


Dec 19, 2021
Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Raynaud, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Linmin Pei, Murat AK, Sarahi Rosas-González, Illyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh McHugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-Andr Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

Figure 1 for QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results
Figure 2 for QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results
Figure 3 for QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results
Figure 4 for QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results
* Under submission at MELBA journal 
  Access Paper or Ask Questions

Decomposing Representations for Deterministic Uncertainty Estimation


Dec 01, 2021
Haiwen Huang, Joost van Amersfoort, Yarin Gal

Figure 1 for Decomposing Representations for Deterministic Uncertainty Estimation
Figure 2 for Decomposing Representations for Deterministic Uncertainty Estimation
Figure 3 for Decomposing Representations for Deterministic Uncertainty Estimation
Figure 4 for Decomposing Representations for Deterministic Uncertainty Estimation

  Access Paper or Ask Questions

DeDUCE: Generating Counterfactual Explanations Efficiently


Nov 29, 2021
Benedikt Höltgen, Lisa Schut, Jan M. Brauner, Yarin Gal

Figure 1 for DeDUCE: Generating Counterfactual Explanations Efficiently
Figure 2 for DeDUCE: Generating Counterfactual Explanations Efficiently
Figure 3 for DeDUCE: Generating Counterfactual Explanations Efficiently
Figure 4 for DeDUCE: Generating Counterfactual Explanations Efficiently
* Presented at the 1st Workshop on eXplainable AI approaches for debugging and diagnosis ([email protected]) 
  Access Paper or Ask Questions

Contrastive Representation Learning with Trainable Augmentation Channel


Nov 15, 2021
Masanori Koyama, Kentaro Minami, Takeru Miyato, Yarin Gal

Figure 1 for Contrastive Representation Learning with Trainable Augmentation Channel
Figure 2 for Contrastive Representation Learning with Trainable Augmentation Channel
Figure 3 for Contrastive Representation Learning with Trainable Augmentation Channel
Figure 4 for Contrastive Representation Learning with Trainable Augmentation Channel

  Access Paper or Ask Questions

Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation


Nov 05, 2021
Muhammed Razzak, Gonzalo Mateo-Garcia, Luis Gómez-Chova, Yarin Gal, Freddie Kalaitzis

Figure 1 for Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation
Figure 2 for Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation
Figure 3 for Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation
Figure 4 for Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation

  Access Paper or Ask Questions

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data


Nov 03, 2021
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal

Figure 1 for Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Figure 2 for Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Figure 3 for Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Figure 4 for Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
* 24 pages, 8 Figures, 5 tables, NeurIPS 2021 
  Access Paper or Ask Questions

Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific


Nov 03, 2021
Andrew Jesson, Peter Manshausen, Alyson Douglas, Duncan Watson-Parris, Yarin Gal, Philip Stier

Figure 1 for Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific
Figure 2 for Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific
Figure 3 for Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific
Figure 4 for Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific

  Access Paper or Ask Questions

Deep Deterministic Uncertainty for Semantic Segmentation


Oct 29, 2021
Jishnu Mukhoti, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal

Figure 1 for Deep Deterministic Uncertainty for Semantic Segmentation
Figure 2 for Deep Deterministic Uncertainty for Semantic Segmentation
Figure 3 for Deep Deterministic Uncertainty for Semantic Segmentation
Figure 4 for Deep Deterministic Uncertainty for Semantic Segmentation

  Access Paper or Ask Questions

GeneDisco: A Benchmark for Experimental Design in Drug Discovery


Oct 22, 2021
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab

Figure 1 for GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Figure 2 for GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Figure 3 for GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Figure 4 for GeneDisco: A Benchmark for Experimental Design in Drug Discovery

  Access Paper or Ask Questions

Quantifying Uncertainty for Machine Learning Based Diagnostic


Jul 29, 2021
Owen Convery, Lewis Smith, Yarin Gal, Adi Hanuka

Figure 1 for Quantifying Uncertainty for Machine Learning Based Diagnostic
Figure 2 for Quantifying Uncertainty for Machine Learning Based Diagnostic
Figure 3 for Quantifying Uncertainty for Machine Learning Based Diagnostic
Figure 4 for Quantifying Uncertainty for Machine Learning Based Diagnostic
* arXiv admin note: substantial text overlap with arXiv:2105.04654 
  Access Paper or Ask Questions

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks


Jul 23, 2021
Andrey Malinin, Neil Band, Ganshin, Alexander, German Chesnokov, Yarin Gal, Mark J. F. Gales, Alexey Noskov, Andrey Ploskonosov, Liudmila Prokhorenkova, Ivan Provilkov, Vatsal Raina, Vyas Raina, Roginskiy, Denis, Mariya Shmatova, Panos Tigas, Boris Yangel

Figure 1 for Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
Figure 2 for Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
Figure 3 for Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
Figure 4 for Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks

  Access Paper or Ask Questions

Prioritized training on points that are learnable, worth learning, and not yet learned


Jul 06, 2021
Sören Mindermann, Muhammed Razzak, Winnie Xu, Andreas Kirsch, Mrinank Sharma, Adrien Morisot, Aidan N. Gomez, Sebastian Farquhar, Jan Brauner, Yarin Gal

Figure 1 for Prioritized training on points that are learnable, worth learning, and not yet learned
Figure 2 for Prioritized training on points that are learnable, worth learning, and not yet learned
Figure 3 for Prioritized training on points that are learnable, worth learning, and not yet learned
Figure 4 for Prioritized training on points that are learnable, worth learning, and not yet learned
* ICML 2021 Workshop on Subset Selection in Machine Learning 
  Access Paper or Ask Questions

Improving black-box optimization in VAE latent space using decoder uncertainty


Jun 30, 2021
Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal

Figure 1 for Improving black-box optimization in VAE latent space using decoder uncertainty
Figure 2 for Improving black-box optimization in VAE latent space using decoder uncertainty
Figure 3 for Improving black-box optimization in VAE latent space using decoder uncertainty
Figure 4 for Improving black-box optimization in VAE latent space using decoder uncertainty

  Access Paper or Ask Questions

A Practical & Unified Notation for Information-Theoretic Quantities in ML


Jun 22, 2021
Andreas Kirsch, Yarin Gal

Figure 1 for A Practical & Unified Notation for Information-Theoretic Quantities in ML
Figure 2 for A Practical & Unified Notation for Information-Theoretic Quantities in ML
Figure 3 for A Practical & Unified Notation for Information-Theoretic Quantities in ML
Figure 4 for A Practical & Unified Notation for Information-Theoretic Quantities in ML

  Access Paper or Ask Questions

A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions


Jun 22, 2021
Andreas Kirsch, Sebastian Farquhar, Yarin Gal

Figure 1 for A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions
Figure 2 for A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions
Figure 3 for A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions

  Access Paper or Ask Questions

Active Learning under Pool Set Distribution Shift and Noisy Data


Jun 22, 2021
Andreas Kirsch, Tom Rainforth, Yarin Gal

Figure 1 for Active Learning under Pool Set Distribution Shift and Noisy Data
Figure 2 for Active Learning under Pool Set Distribution Shift and Noisy Data
Figure 3 for Active Learning under Pool Set Distribution Shift and Noisy Data
Figure 4 for Active Learning under Pool Set Distribution Shift and Noisy Data

  Access Paper or Ask Questions

Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective


Jun 17, 2021
Lewis Smith, Joost van Amersfoort, Haiwen Huang, Stephen Roberts, Yarin Gal

Figure 1 for Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective
Figure 2 for Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective
Figure 3 for Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective
Figure 4 for Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective
* Main paper 10 pages including references, appendix 10 pages. 7 figures and 6 tables including appendix 
  Access Paper or Ask Questions

KL Guided Domain Adaptation


Jun 14, 2021
A. Tuan Nguyen, Toan Tran, Yarin Gal, Philip H. S. Torr, Atılım Güneş Baydin

Figure 1 for KL Guided Domain Adaptation
Figure 2 for KL Guided Domain Adaptation
Figure 3 for KL Guided Domain Adaptation
Figure 4 for KL Guided Domain Adaptation

  Access Paper or Ask Questions

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

Figure 1 for Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Figure 2 for Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Figure 3 for Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Figure 4 for Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning

  Access Paper or Ask Questions

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning


Jun 04, 2021
Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, Yarin Gal

Figure 1 for Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
Figure 2 for Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
Figure 3 for Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
Figure 4 for Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
* First two authors contributed equally 
  Access Paper or Ask Questions

Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization


May 05, 2021
Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz-Rodríguez, Océane Boulais, Aruna Sankaranarayanan, Aaron Piña, Yarin Gal, Chedy Raïssi, Alexander Lavin, Dava Newman

Figure 1 for Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization
Figure 2 for Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization
Figure 3 for Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization
Figure 4 for Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization
* arXiv admin note: text overlap with arXiv:2010.08103 
  Access Paper or Ask Questions

Outcome-Driven Reinforcement Learning via Variational Inference


Apr 20, 2021
Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister, Yarin Gal, Sergey Levine

Figure 1 for Outcome-Driven Reinforcement Learning via Variational Inference
Figure 2 for Outcome-Driven Reinforcement Learning via Variational Inference
Figure 3 for Outcome-Driven Reinforcement Learning via Variational Inference
Figure 4 for Outcome-Driven Reinforcement Learning via Variational Inference

  Access Paper or Ask Questions

Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties


Mar 16, 2021
Lisa Schut, Oscar Key, Rory McGrath, Luca Costabello, Bogdan Sacaleanu, Medb Corcoran, Yarin Gal

Figure 1 for Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Figure 2 for Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Figure 3 for Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Figure 4 for Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
* Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021 
 * 21 pages, 13 Figures 
  Access Paper or Ask Questions

Robustness to Pruning Predicts Generalization in Deep Neural Networks


Mar 10, 2021
Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, Yarin Gal

Figure 1 for Robustness to Pruning Predicts Generalization in Deep Neural Networks
Figure 2 for Robustness to Pruning Predicts Generalization in Deep Neural Networks
Figure 3 for Robustness to Pruning Predicts Generalization in Deep Neural Networks
Figure 4 for Robustness to Pruning Predicts Generalization in Deep Neural Networks

  Access Paper or Ask Questions

Active Testing: Sample-Efficient Model Evaluation


Mar 09, 2021
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth

Figure 1 for Active Testing: Sample-Efficient Model Evaluation
Figure 2 for Active Testing: Sample-Efficient Model Evaluation
Figure 3 for Active Testing: Sample-Efficient Model Evaluation
Figure 4 for Active Testing: Sample-Efficient Model Evaluation

  Access Paper or Ask Questions