1 code implementation • IWCS (ACL) 2021 • Aikaterini-Lida Kalouli, Rebecca Kehlbeck, Rita Sevastjanova, Oliver Deussen, Daniel Keim, Miriam Butt
Research in NLP has mainly focused on factoid questions, with the goal of finding quick and reliable ways of matching a query to an answer.
1 code implementation • 28 Mar 2024 • Yu Xu, Fan Tang, Juan Cao, Yuxin Zhang, Oliver Deussen, WeiMing Dong, Jintao Li, Tong-Yee Lee
Based on the adapters broken apart for separate training content and style, we then make the entity parameter space by reconstructing the content and style PLPs matrices, followed by fine-tuning the combined adapter to generate the target object with the desired appearance.
no code implementations • 12 Mar 2024 • Thilo Spinner, Rebecca Kehlbeck, Rita Sevastjanova, Tobias Stähle, Daniel A. Keim, Oliver Deussen, Mennatallah El-Assady
Large language models (LLMs) are widely deployed in various downstream tasks, e. g., auto-completion, aided writing, or chat-based text generation.
no code implementations • 13 Feb 2024 • Felix Petersen, Aashwin Mishra, Hilde Kuehne, Christian Borgelt, Oliver Deussen, Mikhail Yurochkin
We propose a new approach for propagating stable probability distributions through neural networks.
no code implementations • 17 Oct 2023 • Thilo Spinner, Rebecca Kehlbeck, Rita Sevastjanova, Tobias Stähle, Daniel A. Keim, Oliver Deussen, Andreas Spitz, Mennatallah El-Assady
We quantitatively show the value of exposing the beam search tree and present five detailed analysis scenarios addressing the identified challenges.
2 code implementations • 29 Aug 2023 • Urs Waldmann, Alex Hoi Hang Chan, Hemal Naik, Máté Nagy, Iain D. Couzin, Oliver Deussen, Bastian Goldluecke, Fumihiro Kano
To the best of our knowledge we are the first to present a framework for 2D/3D animal posture and trajectory tracking that works in both indoor and outdoor environments for up to 10 individuals.
3 code implementations • 25 May 2023 • Yuxin Zhang, WeiMing Dong, Fan Tang, Nisha Huang, Haibin Huang, Chongyang Ma, Tong-Yee Lee, Oliver Deussen, Changsheng Xu
We apply ProSpect in various personalized attribute-aware image generation applications, such as image-guided or text-driven manipulations of materials, style, and layout, achieving previously unattainable results from a single image input without fine-tuning the diffusion models.
1 code implementation • 1 May 2023 • Felix Petersen, Tobias Sutter, Christian Borgelt, Dongsung Huh, Hilde Kuehne, Yuekai Sun, Oliver Deussen
We present ISAAC (Input-baSed ApproximAte Curvature), a novel method that conditions the gradient using selected second-order information and has an asymptotically vanishing computational overhead, assuming a batch size smaller than the number of neurons.
1 code implementation • 15 Oct 2022 • Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen
Recently, research has increasingly focused on developing efficient neural network architectures.
1 code implementation • 15 Jun 2022 • Felix Petersen, Hilde Kuehne, Christian Borgelt, Oliver Deussen
In this work, we relax this assumption and optimize the model for multiple k simultaneously instead of using a single k. Leveraging recent advances in differentiable sorting and ranking, we propose a differentiable top-k cross-entropy classification loss.
Ranked #58 on Image Classification on ImageNet
1 code implementation • CVPR 2022 • Felix Petersen, Bastian Goldluecke, Christian Borgelt, Oliver Deussen
In this work, we present and study a generalized family of differentiable renderers.
1 code implementation • ICLR 2022 • Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen
We introduce a family of sigmoid functions and prove that they produce differentiable sorting networks that are monotonic.
no code implementations • 20 Oct 2021 • Felix Petersen, Bastian Goldluecke, Oliver Deussen, Hilde Kuehne
Recently introduced differentiable renderers can be leveraged to learn the 3D geometry of objects from 2D images, but those approaches require additional supervision to enable the renderer to produce an output that can be compared to the input image.
1 code implementation • NeurIPS 2021 • Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen
The integration of algorithmic components into neural architectures has gained increased attention recently, as it allows training neural networks with new forms of supervision such as ordering constraints or silhouettes instead of using ground truth labels.
no code implementations • 29 Sep 2021 • Felix Petersen, Christian Borgelt, Mikhail Yurochkin, Hilde Kuehne, Oliver Deussen
We propose a new approach to propagating probability distributions through neural networks.
no code implementations • 29 Sep 2021 • Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen
We propose a sampling-free approximate formulation of Gaussian variational auto-encoders.
1 code implementation • 9 May 2021 • Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen
Sorting and ranking supervision is a method for training neural networks end-to-end based on ordering constraints.
no code implementations • 3 Mar 2021 • Ruizhen Hu, Bin Chen, Juzhan Xu, Oliver van Kaick, Oliver Deussen, Hui Huang
Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within the entire set.
no code implementations • 12 Aug 2020 • Till Niese, Sören Pirk, Matthias Albrecht, Bedrich Benes, Oliver Deussen
The placement of vegetation plays a central role in the realism of virtual scenes.
no code implementations • NeurIPS Workshop Deep_Invers 2019 • Felix Petersen, Christian Borgelt, Oliver Deussen
Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems.
no code implementations • 1 Aug 2019 • Mennatallah El-Assady, Rebecca Kehlbeck, Christopher Collins, Daniel Keim, Oliver Deussen
We present a framework that allows users to incorporate the semantics of their domain knowledge for topic model refinement while remaining model-agnostic.
no code implementations • 16 May 2019 • Felix Petersen, Christian Borgelt, Oliver Deussen
These networks integrate smooth versions of classic algorithms into the topology of neural networks.
1 code implementation • 3 May 2019 • Jochen Görtler, Thilo Spinner, Dirk Streeb, Daniel Weiskopf, Oliver Deussen
We present a technique to perform dimensionality reduction on data that is subject to uncertainty.
no code implementations • 26 Mar 2019 • Felix Petersen, Amit H. Bermano, Oliver Deussen, Daniel Cohen-Or
The long-coveted task of reconstructing 3D geometry from images is still a standing problem.