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Publications by Hossein Azizpour

Peer reviewed

Articles

[1]
L. Guastoni et al., "Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers," Theoretical and Computational Fluid Dynamics, vol. 39, no. 1, 2025.
[2]
R. Yadav et al., "Unsupervised flood detection on SAR time series using variational autoencoder," International Journal of Applied Earth Observation and Geoinformation, vol. 126, 2024.
[4]
M. Gamba et al., "Deep Double Descent via Smooth Interpolation," Transactions on Machine Learning Research, vol. 4, 2023.
[5]
L. Guastoni et al., "Deep reinforcement learning for turbulent drag reduction in channel flows," The European Physical Journal E Soft matter, vol. 46, no. 4, 2023.
[6]
E. Englesson, A. Mehrpanah and H. Azizpour, "Logistic-Normal Likelihoods for Heteroscedastic Label Noise," Transactions on Machine Learning Research, vol. 8, 2023.
[7]
A. Geetha Balasubramanian et al., "Predicting the wall-shear stress and wall pressure through convolutional neural networks," International Journal of Heat and Fluid Flow, vol. 103, 2023.
[8]
A. Maki et al., "In Memoriam : Jan-Olof Eklundh," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, pp. 4488-4489, 2022.
[9]
S. Hafner et al., "Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection Using a Dual Stream U-Net," IEEE Geoscience and Remote Sensing Letters, vol. 19, 2022.
[10]
L. Guastoni et al., "Convolutional-network models to predict wall-bounded turbulence from wall quantities," Journal of Fluid Mechanics, vol. 928, 2021.
[11]
A. Guemes et al., "From coarse wall measurements to turbulent velocity fields through deep learning," Physics of fluids, vol. 33, no. 7, 2021.
[12]
[14]
F. Baldassarre et al., "GraphQA: Protein Model Quality Assessment using Graph Convolutional Networks," Bioinformatics, vol. 37, no. 3, pp. 360-366, 2020.
[15]
R. Vinuesa et al., "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, vol. 11, no. 1, 2020.
[16]
P. A. Srinivasan et al., "Predictions of turbulent shear flows using deep neural networks," Physical Review Fluids, vol. 4, no. 5, 2019.
[17]
S. Robertson et al., "Digital image analysis in breast pathology-from image processing techniques to artificial intelligence," Translational Research : The Journal of Laboratory and Clinical Medicine, vol. 194, pp. 19-35, 2018.
[19]
H. Azizpour et al., "Factors of Transferability for a Generic ConvNet Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 9, pp. 1790-1802, 2016.

Conference papers

[20]
A. Mehrpanah, E. Englesson and H. Azizpour, "On Spectral Properties of Gradient-Based Explanation Methods," in Computer Vision – ECCV 2024 - 18th European Conference, Proceedings, 2025, pp. 282-299.
[21]
A. Nilsson et al., "Indirectly Parameterized Concrete Autoencoders," in International Conference on Machine Learning, ICML 2024, 2024, pp. 38237-38252.
[22]
A. Nilsson and H. Azizpour, "Regularizing and Interpreting Vision Transformers by Patch Selection on Echocardiography Data," in Proceedings of the 5th Conference on Health, Inference, and Learning, CHIL 2024, 2024, pp. 155-168.
[23]
A. Nilsson and H. Azizpour, "Regularizing and Interpreting Vision Transformers by Patch Selection on Echocardiography Data," in CONFERENCE ON HEALTH, INFERENCE, AND LEARNING, 2024, pp. 155-168.
[24]
E. Englesson and H. Azizpour, "Robust Classification via Regression for Learning with Noisy Labels," in Proceedings ICLR 2024 - The Twelfth International Conference on Learning Representations, 2024.
[25]
H. Hu, F. Baldassarre and H. Azizpour, "Learnable Masked Tokens for Improved Transferability of Self-supervised Vision Transformers," in Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part III, 2023, pp. 409-426.
[26]
M. Gamba, H. Azizpour and M. Björkman, "On the Lipschitz Constant of Deep Networks and Double Descent," in Proceedings 34th British Machine Vision Conference 2023, 2023.
[27]
Y. Liu et al., "PatchDropout : Economizing Vision Transformers Using Patch Dropout," in 2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2023, pp. 3942-3951.
[28]
R. Yadav et al., "Self-Supervised Contrastive Model for Flood Mapping and Monitoring on SAR Time-Series," in EGU23 General Assembly, Vienna, Austria & Online, 23–28 April 2023, 2023.
[29]
M. B. Colomer et al., "To Adapt or Not to Adapt? : Real-Time Adaptation for Semantic Segmentation," in 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2023, pp. 16502-16513.
[30]
M. Gamba et al., "Are All Linear Regions Created Equal?," in Proceedings 25th International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 2022.
[31]
L. Guastoni et al., "Non-Intrusive Sensing in Turbulent Boundary Layers via Deep Fully-Convolutional Neural Networks," in 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022, 2022.
[32]
M. Sorkhei et al., "CSAW-M : An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer," in Conference on Neural Information Processing Systems (NeurIPS) – Datasets and Benchmarks Proceedings, 2021., 2021.
[33]
E. Englesson and H. Azizpour, "Consistency Regularization Can Improve Robustness to Label Noise," in International Conference on Machine Learning (ICML) Workshops, 2021 Workshop on Uncertainty and Robustness in Deep Learning, 2021.
[34]
E. Englesson and H. Azizpour, "Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels," in Proceedings 35th Conference on Neural Information Processing Systems (NeurIPS 2021)., 2021.
[35]
Y. Liu et al., "Decoupling Inherent Risk and Early Cancer Signs in Image-Based Breast Cancer Risk Models," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 : 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI (Lecture Notes in Computer Science), 2020, pp. 230-240.
[36]
F. Baldassarre et al., "Explanation-Based Weakly-Supervised Learning of Visual Relations with Graph Networks," in Proceedings, Part XXVIII Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, 2020, pp. 612-630.
[37]
L. Guastoni et al., "Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks," in Journal of Physics : Conference Series, 2020, p. 012022.
[38]
E. Englesson and H. Azizpour, "Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation," in International Conference on Machine Learning (ICML) Workshops, 2019 Workshop on Uncertainty and Robustness in Deep Learning, 2019.
[39]
F. Baldassarre and H. Azizpour, "Explainability Techniques for Graph Convolutional Networks," in International Conference on Machine Learning (ICML) Workshops, 2019 Workshop on Learning and Reasoning with Graph-Structured Representations, 2019.
[40]
M. Gamba et al., "On the geometry of rectifier convolutional neural networks," in Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2019, pp. 793-797.
[41]
L. Guastoni et al., "On the use of recurrent neural networks for predictions of turbulent flows," in 11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019, 2019.
[42]
M. Teye, H. Azizpour and K. Smith, "Bayesian Uncertainty Estimation for Batch Normalized Deep Networks," in 35th International Conference on Machine Learning, ICML 2018, 2018.
[43]
S. Carlsson et al., "The Preimage of Rectifier Network Activities," in International Conference on Learning Representations (ICLR), 2017.
[44]
H. Azizpour et al., "From Generic to Specific Deep Representations for Visual Recognition," in Proceedings of CVPR 2015, 2015.
[45]
A. Sharif Razavian et al., "Persistent Evidence of Local Image Properties in Generic ConvNets," in Image Analysis : 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings, 2015, pp. 249-262.
[46]
H. Azizpour et al., "Spotlight the Negatives : A Generalized Discriminative Latent Model," in British Machine Vision Conference (BMVC),7-10 September, Swansea, UK, 2015, 2015.
[47]
A. Sharif Razavian et al., "CNN features off-the-shelf : An Astounding Baseline for Recognition," in Proceedings of CVPR 2014, 2014.
[48]
V. Kazemi et al., "Multi-view body part recognition with random forests," in BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013, 2013.
[49]
O. Aghazadeh et al., "Mixture component identification and learning for visual recognition," in Computer Vision – ECCV 2012 : 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI, 2012, pp. 115-128.
[50]
H. Azizpour and I. Laptev, "Object detection using strongly-supervised deformable part models," in Computer Vision – ECCV 2012 : 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I, 2012, pp. 836-849.

Non-peer reviewed

Chapters in books

[51]
B. Sirmacek et al., "The Potential of Artificial Intelligence for Achieving Healthy and Sustainable Societies," in The Ethics of Artificial Intelligence for the Sustainable Development Goals, Francesca Mazzi, Luciano Floridi Ed., : Springer Nature, 2023, pp. 65-96.

Theses

[52]
H. Azizpour, "Visual Representations and Models: From Latent SVM to Deep Learning," Doctoral thesis Stockholm, Sweden : KTH Royal Institute of Technology, TRITA-CSC-A, 21, 2016.
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2025-02-16 02:33:41