Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Wagner, Nicolas"'
Publikováno v:
COLM 2024
We explore on-device self-supervised collaborative fine-tuning of large language models with limited local data availability. Taking inspiration from the collaborative learning community, we introduce three distinct trust-weighted gradient aggregatio
Externí odkaz:
http://arxiv.org/abs/2404.09753
Computationally weak systems and demanding graphical applications are still mostly dependent on linear blendshapes for facial animations. The accompanying artifacts such as self-intersections, loss of volume, or missing soft tissue elasticity can be
Externí odkaz:
http://arxiv.org/abs/2212.14784
The next step for intelligent dialog agents is to escape their role as silent bystanders and become proactive. Well-defined proactive behavior may improve human-machine cooperation, as the agent takes a more active role during interaction and takes o
Externí odkaz:
http://arxiv.org/abs/2211.15359
Although deep federated learning has received much attention in recent years, progress has been made mainly in the context of natural images and barely for computational pathology. However, deep federated learning is an opportunity to create datasets
Externí odkaz:
http://arxiv.org/abs/2209.14849
Publikováno v:
In Médecine Palliative Soins de Support - Accompagnement - Ethique September 2024 23(5):217-224
Publikováno v:
In Computers & Graphics August 2024 122
With the increased availability of 3D data, the need for solutions processing those also increased rapidly. However, adding dimension to already reliably accurate 2D approaches leads to immense memory consumption and higher computational complexity.
Externí odkaz:
http://arxiv.org/abs/2112.03917
Autor:
Wagner, Nicolas, Schwanecke, Ulrich
In this paper, we propose NeuralQAAD, a differentiable point cloud compression framework that is fast, robust to sampling, and applicable to high resolutions. Previous work that is able to handle complex and non-smooth topologies is hardly scaleable
Externí odkaz:
http://arxiv.org/abs/2012.08143
Autor:
Burkhardt, Sophie, Brugger, Jannis, Wagner, Nicolas, Ahmadi, Zahra, Kersting, Kristian, Kramer, Stefan
Most deep neural networks are considered to be black boxes, meaning their output is hard to interpret. In contrast, logical expressions are considered to be more comprehensible since they use symbols that are semantically close to natural language in
Externí odkaz:
http://arxiv.org/abs/2012.08459
Autor:
Wagner, Nicolas, Mukhopadhyay, Anirban
Super-Selfish is an easy to use PyTorch framework for image-based self-supervised learning. Features can be learned with 13 algorithms that span from simple classification to more complex state of theart contrastive pretext tasks. The framework is ea
Externí odkaz:
http://arxiv.org/abs/2012.02706