no code implementations • 3 Dec 2024 • Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis, Daniel Rueckert, Gintare Karolina Dziugaite, Eleni Triantafillou
We also propose a new unlearning algorithm, Deletion by Example Localization (DEL), that resets the parameters deemed-to-be most critical according to our localization strategy, and then finetunes them.
no code implementations • 11 Oct 2022 • Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Daniel Rueckert, Georgios Kaissis
Identifying the samples with noisy labels and preventing the model from learning them is a promising approach to address this challenge.
1 code implementation • 20 May 2022 • Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Daniel Rueckert, Georgios Kaissis
Existing convolutional neural network architectures frequently rely upon batch normalization (BatchNorm) to effectively train the model.
2 code implementations • 21 May 2021 • Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Julian Matschinske, Jan Baumbach, Daniel Rueckert, Georgios Kaissis
Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server.
1 code implementation • 12 May 2021 • Julian Matschinske, Julian Späth, Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Anne Hartebrodt, Balázs Orbán, Sándor Fejér, Olga Zolotareva, Mohammad Bakhtiari, Béla Bihari, Marcus Bloice, Nina C Donner, Walid Fdhila, Tobias Frisch, Anne-Christin Hauschild, Dominik Heider, Andreas Holzinger, Walter Hötzendorfer, Jan Hospes, Tim Kacprowski, Markus Kastelitz, Markus List, Rudolf Mayer, Mónika Moga, Heimo Müller, Anastasia Pustozerova, Richard Röttger, Anna Saranti, Harald HHW Schmidt, Christof Tschohl, Nina K Wenke, Jan Baumbach
Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data.
no code implementations • 13 Nov 2020 • Reza Nasirigerdeh, Mohammad Bakhtiari, Reihaneh Torkzadehmahani, Amirhossein Bayat, Markus List, David B. Blumenthal, Jan Baumbach
Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) across the clients.
1 code implementation • 30 Oct 2020 • Olga Zolotareva, Reza Nasirigerdeh, Julian Matschinske, Reihaneh Torkzadehmahani, Tobias Frisch, Julian Späth, David B. Blumenthal, Amir Abbasinejad, Paolo Tieri, Nina K. Wenke, Markus List, Jan Baumbach
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights.
no code implementations • 22 Jul 2020 • Reihaneh Torkzadehmahani, Reza Nasirigerdeh, David B. Blumenthal, Tim Kacprowski, Markus List, Julian Matschinske, Julian Späth, Nina Kerstin Wenke, Béla Bihari, Tobias Frisch, Anne Hartebrodt, Anne-Christin Hausschild, Dominik Heider, Andreas Holzinger, Walter Hötzendorfer, Markus Kastelitz, Rudolf Mayer, Cristian Nogales, Anastasia Pustozerova, Richard Röttger, Harald H. H. W. Schmidt, Ameli Schwalber, Christof Tschohl, Andrea Wohner, Jan Baumbach
Artificial intelligence (AI) has been successfully applied in numerous scientific domains.
1 code implementation • 27 Jan 2020 • Reihaneh Torkzadehmahani, Peter Kairouz, Benedict Paten
Generative Adversarial Networks (GANs) are one of the well-known models to generate synthetic data including images, especially for research communities that cannot use original sensitive datasets because they are not publicly accessible.