no code implementations • 10 Apr 2024 • Weihao Xia, Raoul de Charette, Cengiz Öztireli, Jing-Hao Xue
We address prevailing challenges of the brain-powered research, departing from the observation that the literature hardly recover accurate spatial information and require subject-specific models.
no code implementations • 7 Feb 2024 • Xingchang Huang, Corentin Salaün, Cristina Vasconcelos, Christian Theobalt, Cengiz Öztireli, Gurprit Singh
In this paper, we introduce a novel and general class of diffusion models taking correlated noise within and across images into account.
2 code implementations • 2 Feb 2024 • Jack Foster, Kyle Fogarty, Stefan Schoepf, Cengiz Öztireli, Alexandra Brintrup
The key challenge in unlearning is forgetting the necessary data in a timely manner, while preserving model performance.
no code implementations • 3 Oct 2023 • Weihao Xia, Raoul de Charette, Cengiz Öztireli, Jing-Hao Xue
In this work we present DREAM, an fMRI-to-image method for reconstructing viewed images from brain activities, grounded on fundamental knowledge of the human visual system.
no code implementations • 23 Sep 2022 • Karoline Heiwolt, Cengiz Öztireli, Grzegorz Cielniak
We present a landmark-free shape compression algorithm, which allows for the extraction of 3D shape features of leaves, characterises leaf shape and curvature efficiently in few parameters, and makes the association of individual leaves in feature space possible.
1 code implementation • 25 Nov 2021 • Ana Dodik, Marios Papas, Cengiz Öztireli, Thomas Müller
In particular, we approximate incident radiance as an online-trained $5$D mixture that is accelerated by a $k$D-tree.
1 code implementation • 2 Jun 2020 • Marco Ancona, Cengiz Öztireli, Markus Gross
The usual pruning pipeline consists of ranking the network internal filters and activations with respect to their contributions to the network performance, removing the units with the lowest contribution, and fine-tuning the network to reduce the harm induced by pruning.
1 code implementation • 10 Jun 2019 • Wang Yifan, Felice Serena, Shihao Wu, Cengiz Öztireli, Olga Sorkine-Hornung
We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for point clouds.
1 code implementation • 26 Mar 2019 • Marco Ancona, Cengiz Öztireli, Markus Gross
The problem of explaining the behavior of deep neural networks has recently gained a lot of attention.
1 code implementation • 8 Apr 2018 • Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi
We first show that the phenomenon of variance reduction by diversified sampling generalizes in particular to non-stationary point processes.
2 code implementations • ICLR 2018 • Marco Ancona, Enea Ceolini, Cengiz Öztireli, Markus Gross
Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years.