Andrey Malinin

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All published works

Action	Title	Year	Authors
+ PDF Chat	Tackling Bias in the Dice Similarity Coefficient: Introducing NDSC for White Matter Lesion Segmentation	2023	Vatsal Raina Nataliia Molchanova Mara Graziani Andrey Malinin Henning Müller Meritxell Bach Cuadra Mark Gales
+	Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation	2023	Vatsal Raina Nataliia Molchanova Mara Graziani Andrey Malinin Henning Müller Meritxell Bach Cuadra Mark Gales
+	Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts	2023	Gleb Bazhenov Denis Kuznedelev Andrey Malinin Artem Babenko Liudmila Prokhorenkova
+	Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation	2023	Nataliia Molchanova Vatsal Raina Andrey Malinin Francesco La Rosa Adrien Depeursinge Mark Gales Cristina Granziera Henning Müller Mara Graziani Meritxell Bach Cuadra
+	Shifts Marine Cargo Vessel Power Consumption Prediction Dataset	2022	Andrey Malinin Ανδρέας Αθανασόπουλος Muhamed Baraković Meritxell Bach Cuadra Mark Gales Cristina Granziera Mara Graziani Nikolay Kartashev Konstantinos G. Kyriakopoulos Po‐Jui Lu
+	Shifts Marine Cargo Vessel Power Consumption Prediction Dataset	2022	Andrey Malinin Ανδρέας Αθανασόπουλος Muhamed Baraković Meritxell Bach Cuadra Mark Gales Cristina Granziera Mara Graziani Nikolay Kartashev Konstantinos G. Kyriakopoulos Po‐Jui Lu
+	Shifts Marine Cargo Vessel Power Consumption Prediction Dataset	2022	Andrey Malinin Ανδρέας Αθανασόπουλος Muhamed Baraković Meritxell Bach Cuadra Mark Gales Cristina Granziera Mara Graziani Nikolay Kartashev Konstantinos G. Kyriakopoulos Po‐Jui Lu
+	Shifts Multiple Sclerosis Lesion Segmentation Dataset Part 1	2022	Andrey Malinin Ανδρέας Αθανασόπουλος Muhamed Baraković Meritxell Bach Cuadra Mark Gales Cristina Granziera Mara Graziani Nikolay Kartashev Konstantinos G. Kyriakopoulos Po‐Jui Lu
+	Shifts Multiple Sclerosis Lesion Segmentation Dataset Part 2	2022	Andrey Malinin Ανδρέας Αθανασόπουλος Muhamed Baraković Meritxell Bach Cuadra Mark Gales Cristina Granziera Mara Graziani Nikolay Kartashev Konstantinos G. Kyriakopoulos Po‐Jui Lu
+	Shifts Multiple Sclerosis Lesion Segmentation Dataset Part 2	2022	Andrey Malinin Ανδρέας Αθανασόπουλος Muhamed Baraković Meritxell Bach Cuadra Mark Gales Cristina Granziera Mara Graziani Nikolay Kartashev Konstantinos G. Kyriakopoulos Po‐Jui Lu
+	Shifts Multiple Sclerosis Lesion Segmentation Dataset Part 1	2022	Andrey Malinin Ανδρέας Αθανασόπουλος Muhamed Baraković Meritxell Bach Cuadra Mark Gales Cristina Granziera Mara Graziani Nikolay Kartashev Konstantinos G. Kyriakopoulos Po‐Jui Lu
+	Shifts 2.0: Extending The Dataset of Real Distributional Shifts	2022	Andrey Malinin Ανδρέας Αθανασόπουλος Muhamed Baraković Meritxell Bach Cuadra Mark Gales Cristina Granziera Mara Graziani Nikolay Kartashev Konstantinos G. Kyriakopoulos Po‐Jui Lu
+	Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis	2022	Nataliia Molchanova Vatsal Raina Andrey Malinin Francesco La Rosa Henning Müller Mark Gales Cristina Granziera Mara Graziani Meritxell Bach Cuadra
+	Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets	2021	Max Ryabinin Andrey Malinin Mark Gales
+	Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks	2021	Andrey Malinin Neil Band Ganshin Alexander - German Chesnokov Yarin Gal Mark Gales Alexey Noskov Andrey Ploskonosov Liudmila Prokhorenkova
+	On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay	2021	Ekaterina Lobacheva Maxim Kodryan Nadezhda Chirkova Andrey Malinin Dmitry Vetrov
+ PDF Chat	Ensemble Distillation Approaches for Grammatical Error Correction	2021	Yassir Fathullah Mark Gales Andrey Malinin
+	Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets	2021	Max Ryabinin Andrey Malinin Mark Gales
+ PDF Chat	Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance	2021	Carel van Niekerk Andrey Malinin Christian Geishauser Michael Heck Hsien-chin Lin Nurul Lubis Shutong Feng Milica Gašić
+ PDF Chat	Multi-Sentence Resampling: A Simple Approach to Alleviate Dataset Length Bias and Beam-Search Degradation	2021	Ivan Provilkov Andrey Malinin
+	Multi-Sentence Resampling: A Simple Approach to Alleviate Dataset Length Bias and Beam-Search Degradation	2021	Ivan Provilkov Andrey Malinin
+	Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance	2021	Carel van Niekerk Andrey Malinin Christian Geishauser Michael Heck Hsien-chin Lin Nurul Lubis Shutong Feng Milica Gašić
+	Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks	2021	Andrey Malinin Neil Band Ganshin Alexander - German Chesnokov Yarin Gal Mark Gales Alexey Noskov Andrey Ploskonosov Liudmila Prokhorenkova
+	On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay	2021	Ekaterina Lobacheva Maxim Kodryan Nadezhda Chirkova Andrey Malinin Dmitry Vetrov
+	Ensemble Distillation Approaches for Grammatical Error Correction.	2020	Yassir Fathullah Mark Gales Andrey Malinin
+	Ensemble Distribution Distillation	2020	Andrey Malinin Bruno Mlodozeniec Mark Gales
+	Uncertainty in Structured Prediction.	2020	Andrey Malinin Mark Gales
+	Uncertainty in Gradient Boosting via Ensembles	2020	Aleksei Ustimenko Liudmila Prokhorenkova Andrey Malinin
+	Regression Prior Networks	2020	Andrey Malinin Sergey Chervontsev Ivan Provilkov Mark Gales
+	Uncertainty Estimation in Autoregressive Structured Prediction	2020	Andrey Malinin Mark Gales
+	Ensemble Distillation Approaches for Grammatical Error Correction	2020	Yassir Fathullah Mark Gales Andrey Malinin
+	Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness	2019	Andrey Malinin Mark Gales
+	Ensemble Distribution Distillation	2019	Andrey Malinin Bruno Mlodozeniec Mark Gales
+	Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness	2019	Andrey Malinin Mark Gales
+	Prior Networks for Detection of Adversarial Attacks	2018	Andrey Malinin Mark Gales
+	Predictive Uncertainty Estimation via Prior Networks	2018	Andrey Malinin Mark Gales
+	Prior Networks for Detection of Adversarial Attacks	2018	Andrey Malinin Mark Gales

Common Coauthors

Coauthor	Papers Together
Mark Gales	29
Vatsal Raina	14
Meritxell Bach Cuadra	12
Mara Graziani	12
Nataliia Molchanova	12
Cristina Granziera	10
Francesco La Rosa	10
Muhamed Baraković	8
Nikolay Kartashev	8
Po‐Jui Lu	8
Efi Tsompopoulou	8
Elena Volf	8
Eli Sivena	8
Ανδρέας Αθανασόπουλος	8
Vasileios Tsarsitalidis	8
Konstantinos G. Kyriakopoulos	8
Antonis Nikitakis	8
Ivan Provilkov	5
Liudmila Prokhorenkova	4
Henning Müller	4
Yassir Fathullah	3
Carel van Niekerk	2
Mariya Shmatova	2
Ganshin	2
Michael Heck	2
Roginskiy	2
Vyas Raina	2
Panos Tigas	2
Yarin Gal	2
Neil Band	2
Shutong Feng	2
Nurul Lubis	2
German Chesnokov	2
Nadezhda Chirkova	2
Alexey Noskov	2
Milica Gašić	2
Bruno Mlodozeniec	2
Denis Denis	2
Christian Geishauser	2
Boris Yangel	2
Max Ryabinin	2
Hsien-chin Lin	2
Alexander -	2
Ekaterina Lobacheva	2
Dmitry Vetrov	1
Maxim Kodryan	1
Gleb Bazhenov	1
Sergey Chervontsev	1
Adrien Depeursinge	1
Denis Kuznedelev	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles	2016	Balaji Lakshminarayanan Alexander Pritzel Charles Blundell	11
+	Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning	2015	Yarin Gal Zoubin Ghahramani	11
+	Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift	2019	Yaniv Ovadia Emily Fertig Jie Ren Zachary Nado D. Sculley Sebastian Nowozin Joshua V. Dillon Balaji Lakshminarayanan Jasper Snoek	9
+	Ensemble Distribution Distillation	2020	Andrey Malinin Bruno Mlodozeniec Mark Gales	8
+	Distilling the Knowledge in a Neural Network	2015	Geoffrey E. Hinton Oriol Vinyals Jay B. Dean	8
+	Very Deep Convolutional Networks for Large-Scale Image Recognition	2014	Karen Simonyan Andrew Zisserman	8
+	Efficient Estimation of Word Representations in Vector Space	2013	Tomáš Mikolov Kai Chen Greg S. Corrado Jay B. Dean	8
+	Concrete Problems in AI Safety	2016	Dario Amodei Chris Olah Jacob Steinhardt Paul F. Christiano John Schulman Dan Mané	6
+	Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness	2019	Andrey Malinin Mark Gales	5
+	Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems.	2017	Stefan Depeweg José Miguel Hernández-Lobato Finale Doshi‐Velez Steffen Udluft	5
+	A Simple Baseline for Bayesian Uncertainty in Deep Learning	2019	Wesley J. Maddox Timur Garipov Pavel Izmailov Dmitry Vetrov Andrew Gordon Wilson	5
+	BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning	2019	Andreas Kirsch Joost van Amersfoort Yarin Gal	5
+	Attention is All you Need	2017	Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Łukasz Kaiser Illia Polosukhin	5
+	A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks	2016	Dan Hendrycks Kevin Gimpel	4
+	Neural Machine Translation by Jointly Learning to Align and Translate	2015	Dzmitry Bahdanau Kyunghyun Cho Yoshua Bengio	4
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	4
+ PDF Chat	Neural Machine Translation of Rare Words with Subword Units	2016	Rico Sennrich Barry Haddow Alexandra Birch	4
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	4
+	Deep Speech: Scaling up end-to-end speech recognition	2014	Awni Hannun Carl Case Jared Casper Bryan Catanzaro Greg Diamos Erich Elsen Ryan Prenger Sanjeev Satheesh Shubho Sengupta Adam Coates	4
+	A Call for Clarity in Reporting BLEU Scores	2018	Matt Post	4
+ PDF Chat	fairseq: A Fast, Extensible Toolkit for Sequence Modeling	2019	Myle Ott Sergey Edunov Alexei Baevski Angela Fan Sam Gross Nathan Ng David Grangier Michael Auli	4
+	Deep Ensembles: A Loss Landscape Perspective	2019	Stanislav Fort Huiyi Hu Balaji Lakshminarayanan	3
+	Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness	2019	Andrey Malinin Mark Gales	3
+	Understanding Measures of Uncertainty for Adversarial Example Detection	2018	Lewis Smith Yarin Gal	3
+	Uncertainty in Structured Prediction.	2020	Andrey Malinin Mark Gales	3
+	Predictive Uncertainty Estimation via Prior Networks	2018	Andrey Malinin Mark Gales	3
+	Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks	2013	Ian Goodfellow Yaroslav Bulatov Julian Ibarz Sacha Arnoud Vinay Shet	3
+	A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks	2016	Dan Hendrycks Kevin Gimpel	3
+	Calibration of Encoder Decoder Models for Neural Machine Translation.	2019	Aviral Kumar Sunita Sarawagi	3
+	Attention Is All You Need	2017	Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Łukasz Kaiser Illia Polosukhin	3
+	LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop	2015	Fisher Yu Yinda Zhang Shuran Song Ari Seff Jianxiong Xiao	3
+	Improving Back-Translation with Uncertainty-based Confidence Estimation	2019	Shuo Wang Yang Liu Chao Wang Huanbo Luan Maosong Sun	2
+	Hydra: Preserving Ensemble Diversity for Model Distillation	2020	Linh Tran Bastiaan S. Veeling Kevin A. Roth Jakub Świątkowski Joshua V. Dillon Jasper Snoek Stephan Mandt Tim Salimans Sebastian Nowozin Rodolphe Jenatton	2
+	Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation	2016	Yonghui Wu Mike Schuster Zhifeng Chen Quoc V. Le Mohammad Norouzi Wolfgang Macherey Maxim Krikun Yuan Cao Qin Gao Klaus Macherey	2
+	Towards Evaluating the Robustness of Neural Networks	2016	Nicholas Carlini David Wagner	2
+	Bayesian Dark Knowledge	2015	Anoop Korattikara Vivek Rathod Kevin J. Murphy Max Welling	2
+ PDF Chat	Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks	2016	Nicolas Papernot Patrick McDaniel Xi Wu Somesh Jha Ananthram Swami	2
+ PDF Chat	Boosting Adversarial Attacks with Momentum	2018	Yinpeng Dong Fangzhou Liao Tianyu Pang Hang Su Jun Zhu Xiaolin Hu Jianguo Li	2
+ PDF Chat	Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics	2018	Roberto Cipolla Yarin Gal Alex Kendall	2
+	Distilling Model Knowledge	2015	George Papamakarios	2
+ PDF Chat	Sequence-Level Knowledge Distillation	2016	Yoon Kim Alexander M. Rush	2
+	Ensemble Distribution Distillation	2019	Andrey Malinin Bruno Mlodozeniec Mark Gales	2
+	Learning to Generate Long-term Future via Hierarchical Prediction	2017	Ruben Villegas Shuicheng Yan Yuliang Zou Sungryull Sohn Xunyu Lin Honglak Lee	2
+	Pattern Recognition and Machine Learning	2007	Christopher Bishop	2
+	Adversarial and Clean Data Are Not Twins	2023	Zhitao Gong Wenlu Wang	2
+	Evidential Deep Learning to Quantify Classification Uncertainty	2018	Murat Şensoy Lance Kaplan Melih Kandemir	2
+	Bayesian Learning via Stochastic Gradient Langevin Dynamics	2011	Max Welling Yee Whye Teh	2
+	On the (Statistical) Detection of Adversarial Examples	2017	Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel	2
+	Scaling Neural Machine Translation	2018	Myle Ott Sergey Edunov David Grangier Michael Auli	2
+	On Detecting Adversarial Perturbations	2017	Jan Hendrik Metzen Tim Genewein Volker Fischer Bastian Bischoff	2