Publikationer av Hossein Azizpour

Refereegranskade

Artiklar

[1]

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.

[2]

Y. Liu et al., "Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening," Radiology, vol. 311, no. 1, 2024.

[3]

M. Gamba et al., "Deep Double Descent via Smooth Interpolation," Transactions on Machine Learning Research, vol. 4, 2023.

[4]

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.

[5]

E. Englesson, A. Mehrpanah och H. Azizpour, "Logistic-Normal Likelihoods for Heteroscedastic Label Noise," Transactions on Machine Learning Research, vol. 8, 2023.

[6]

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.

[7]

A. Maki et al., "In Memoriam : Jan-Olof Eklundh," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, s. 4488-4489, 2022.

[8]

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.

[9]

L. Guastoni et al., "Convolutional-network models to predict wall-bounded turbulence from wall quantities," Journal of Fluid Mechanics, vol. 928, 2021.

[10]

A. Guemes et al., "From coarse wall measurements to turbulent velocity fields through deep learning," Physics of fluids, vol. 33, no. 7, 2021.

[11]

H. Eivazi et al., "Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence," International Journal of Heat and Fluid Flow, vol. 90, 2021.

[12]

K. Dembrower et al., "Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction," Radiology, vol. 294, no. 2, s. 265-272, 2020.

[13]

F. Baldassarre et al., "GraphQA: Protein Model Quality Assessment using Graph Convolutional Networks," Bioinformatics, vol. 37, no. 3, s. 360-366, 2020.

[14]

R. Vinuesa et al., "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, vol. 11, no. 1, 2020.

[15]

P. A. Srinivasan et al., "Predictions of turbulent shear flows using deep neural networks," Physical Review Fluids, vol. 4, no. 5, 2019.

[16]

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, s. 19-35, 2018.

[17]

K. Smith et al., "Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays," CELL SYSTEMS, vol. 6, no. 6, s. 636-653, 2018.

[18]

H. Azizpour et al., "Factors of Transferability for a Generic ConvNet Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 9, s. 1790-1802, 2016.

Konferensbidrag

[19]

A. Nilsson et al., "Indirectly Parameterized Concrete Autoencoders," i International Conference on Machine Learning, ICML 2024, 2024, s. 38237-38252.

[20]

A. Nilsson och H. Azizpour, "Regularizing and Interpreting Vision Transformers by Patch Selection on Echocardiography Data," i Proceedings of the 5th Conference on Health, Inference, and Learning, CHIL 2024, 2024, s. 155-168.

[21]

E. Englesson och H. Azizpour, "Robust Classification via Regression for Learning with Noisy Labels," i Proceedings ICLR 2024 - The Twelfth International Conference on Learning Representations, 2024.

[22]

H. Hu, F. Baldassarre och H. Azizpour, "Learnable Masked Tokens for Improved Transferability of Self-supervised Vision Transformers," i Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part III, 2023, s. 409-426.

[23]

M. Gamba, H. Azizpour och M. Björkman, "On the Lipschitz Constant of Deep Networks and Double Descent," i Proceedings 34th British Machine Vision Conference 2023, 2023.

[24]

Y. Liu et al., "PatchDropout : Economizing Vision Transformers Using Patch Dropout," i 2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2023, s. 3942-3951.

[25]

R. Yadav et al., "Self-Supervised Contrastive Model for Flood Mapping and Monitoring on SAR Time-Series," i EGU23 General Assembly, Vienna, Austria & Online, 23–28 April 2023, 2023.

[26]

M. B. Colomer et al., "To Adapt or Not to Adapt? : Real-Time Adaptation for Semantic Segmentation," i 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2023, s. 16502-16513.

[27]

M. Gamba et al., "Are All Linear Regions Created Equal?," i Proceedings 25th International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 2022.

[28]

L. Guastoni et al., "Non-Intrusive Sensing in Turbulent Boundary Layers via Deep Fully-Convolutional Neural Networks," i 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022, 2022.

[29]

M. Sorkhei et al., "CSAW-M : An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer," i Conference on Neural Information Processing Systems (NeurIPS) – Datasets and Benchmarks Proceedings, 2021., 2021.

[30]

E. Englesson och H. Azizpour, "Consistency Regularization Can Improve Robustness to Label Noise," i International Conference on Machine Learning (ICML) Workshops, 2021 Workshop on Uncertainty and Robustness in Deep Learning, 2021.

[31]

E. Englesson och H. Azizpour, "Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels," i Proceedings 35th Conference on Neural Information Processing Systems (NeurIPS 2021)., 2021.

[32]

Y. Liu et al., "Decoupling Inherent Risk and Early Cancer Signs in Image-Based Breast Cancer Risk Models," i 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, s. 230-240.

[33]

F. Baldassarre et al., "Explanation-Based Weakly-Supervised Learning of Visual Relations with Graph Networks," i Proceedings, Part XXVIII Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, 2020, s. 612-630.

[34]

L. Guastoni et al., "Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks," i Journal of Physics : Conference Series, 2020, s. 012022.

[35]

E. Englesson och H. Azizpour, "Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation," i International Conference on Machine Learning (ICML) Workshops, 2019 Workshop on Uncertainty and Robustness in Deep Learning, 2019.

[36]

F. Baldassarre och H. Azizpour, "Explainability Techniques for Graph Convolutional Networks," i International Conference on Machine Learning (ICML) Workshops, 2019 Workshop on Learning and Reasoning with Graph-Structured Representations, 2019.

[37]

M. Gamba et al., "On the geometry of rectifier convolutional neural networks," i Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2019, s. 793-797.

[38]

L. Guastoni et al., "On the use of recurrent neural networks for predictions of turbulent flows," i 11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019, 2019.

[39]

M. Teye, H. Azizpour och K. Smith, "Bayesian Uncertainty Estimation for Batch Normalized Deep Networks," i 35th International Conference on Machine Learning, ICML 2018, 2018.

[40]

S. Carlsson et al., "The Preimage of Rectifier Network Activities," i International Conference on Learning Representations (ICLR), 2017.

[41]

H. Azizpour et al., "From Generic to Specific Deep Representations for Visual Recognition," i Proceedings of CVPR 2015, 2015.

[42]

A. Sharif Razavian et al., "Persistent Evidence of Local Image Properties in Generic ConvNets," i Image Analysis : 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings, 2015, s. 249-262.

[43]

H. Azizpour et al., "Spotlight the Negatives : A Generalized Discriminative Latent Model," i British Machine Vision Conference (BMVC),7-10 September, Swansea, UK, 2015, 2015.

[44]

A. Sharif Razavian et al., "CNN features off-the-shelf : An Astounding Baseline for Recognition," i Proceedings of CVPR 2014, 2014.

[45]

V. Kazemi et al., "Multi-view body part recognition with random forests," i BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013, 2013.

[46]

O. Aghazadeh et al., "Mixture component identification and learning for visual recognition," i Computer Vision – ECCV 2012 : 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI, 2012, s. 115-128.

[47]

H. Azizpour och I. Laptev, "Object detection using strongly-supervised deformable part models," i Computer Vision – ECCV 2012 : 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I, 2012, s. 836-849.

Icke refereegranskade

Kapitel i böcker

[48]

B. Sirmacek et al., "The Potential of Artificial Intelligence for Achieving Healthy and Sustainable Societies," i The Ethics of Artificial Intelligence for the Sustainable Development Goals, Francesca Mazzi, Luciano Floridi red., : Springer Nature, 2023, s. 65-96.

Avhandlingar

[49]

H. Azizpour, "Visual Representations and Models: From Latent SVM to Deep Learning," Doktorsavhandling Stockholm, Sweden : KTH Royal Institute of Technology, TRITA-CSC-A, 21, 2016.