Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Boix, Xavier"'
Autor:
Cooper, Avi, Kato, Keizo, Shih, Chia-Hsien, Yamane, Hiroaki, Vinken, Kasper, Takemoto, Kentaro, Sunagawa, Taro, Yeh, Hao-Wei, Yamanaka, Jin, Mason, Ian, Boix, Xavier
Visual Language Models (VLMs) are now increasingly being merged with Large Language Models (LLMs) to enable new capabilities, particularly in terms of improved interactivity and open-ended responsiveness. While these are remarkable capabilities, the
Externí odkaz:
http://arxiv.org/abs/2410.14690
Configural processing, the perception of spatial relationships among an object's components, is crucial for object recognition. However, the teleology and underlying neurocomputational mechanisms of such processing are still elusive, notwithstanding
Externí odkaz:
http://arxiv.org/abs/2407.19072
Autor:
Ozkan, Ece, Boix, Xavier
Current machine learning methods for medical image analysis primarily focus on developing models tailored for their specific tasks, utilizing data within their target domain. These specialized models tend to be data-hungry and often exhibit limitatio
Externí odkaz:
http://arxiv.org/abs/2310.06737
Autor:
Rahimi, Amir, D'Amario, Vanessa, Yamada, Moyuru, Takemoto, Kentaro, Sasaki, Tomotake, Boix, Xavier
Systematic generalization is a crucial aspect of intelligence, which refers to the ability to generalize to novel tasks by combining known subtasks and concepts. One critical factor that has been shown to influence systematic generalization is the di
Externí odkaz:
http://arxiv.org/abs/2309.08798
By default neural networks are not robust to changes in data distribution. This has been demonstrated with simple image corruptions, such as blurring or adding noise, degrading image classification performance. Many methods have been proposed to miti
Externí odkaz:
http://arxiv.org/abs/2306.09005
Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios. In this paper, we introduce a tool to visualize and understand such failures. We draw inspiration from concepts from neural electrophysiology, which are based on inspecting the
Externí odkaz:
http://arxiv.org/abs/2303.11912
Autor:
Ozkan, Ece1,2 (AUTHOR) ece.oezkanelsen@inf.ethz.ch, Boix, Xavier3 (AUTHOR)
Publikováno v:
Scientific Reports. 10/17/2024, Vol. 14 Issue 1, p1-15. 15p.
Transformers achieve great performance on Visual Question Answering (VQA). However, their systematic generalization capabilities, i.e., handling novel combinations of known concepts, is unclear. We reveal that Neural Module Networks (NMNs), i.e., que
Externí odkaz:
http://arxiv.org/abs/2201.11316
Autor:
Villalobos, Kimberly, Štih, Vilim, Ahmadinejad, Amineh, Sundaram, Shobhita, Dozier, Jamell, Francl, Andrew, Azevedo, Frederico, Sasaki, Tomotake, Boix, Xavier
Publikováno v:
Neural Computation 33 (2021) 2511-2549
The insideness problem is an aspect of image segmentation that consists of determining which pixels are inside and outside a region. Deep Neural Networks (DNNs) excel in segmentation benchmarks, but it is unclear if they have the ability to solve the
Externí odkaz:
http://arxiv.org/abs/2201.10664
Many state-of-the-art adversarial training methods for deep learning leverage upper bounds of the adversarial loss to provide security guarantees against adversarial attacks. Yet, these methods rely on convex relaxations to propagate lower and upper
Externí odkaz:
http://arxiv.org/abs/2112.09279