Zobrazeno 1 - 10
of 311
pro vyhledávání: '"Betke, Margrit"'
Autor:
Chen, Zijian, Varkanitsa, Maria, Ishwar, Prakash, Konrad, Janusz, Betke, Margrit, Kiran, Swathi, Venkataraman, Archana
We propose a lesion-aware graph neural network (LEGNet) to predict language ability from resting-state fMRI (rs-fMRI) connectivity in patients with post-stroke aphasia. Our model integrates three components: an edge-based learning module that encodes
Externí odkaz:
http://arxiv.org/abs/2409.02303
Autor:
Gao, Ge, Kim, Jongin, Paik, Sejin, Novozhilova, Ekaterina, Liu, Yi, Bonna, Sarah T., Betke, Margrit, Wijaya, Derry Tanti
Publikováno v:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) 5944-5955
Predicting emotions elicited by news headlines can be challenging as the task is largely influenced by the varying nature of people's interpretations and backgrounds. Previous works have explored classifying discrete emotions directly from news headl
Externí odkaz:
http://arxiv.org/abs/2407.10091
Autor:
Tourni, Isidora Chara, Guo, Lei, Hu, Hengchang, Halim, Edward, Ishwar, Prakash, Daryanto, Taufiq, Jalal, Mona, Chen, Boqi, Betke, Margrit, Zhafransyah, Fabian, Lai, Sha, Wijaya, Derry Tanti
News media structure their reporting of events or issues using certain perspectives. When describing an incident involving gun violence, for example, some journalists may focus on mental health or gun regulation, while others may emphasize the discus
Externí odkaz:
http://arxiv.org/abs/2406.17213
A major bottleneck of interdisciplinary computer vision (CV) research is the lack of a framework that eases the reuse and abstraction of state-of-the-art CV models by CV and non-CV researchers alike. We present here BU-CVKit, a computer vision framew
Externí odkaz:
http://arxiv.org/abs/2306.04736
While transformers have greatly boosted performance in semantic segmentation, domain adaptive transformers are not yet well explored. We identify that the domain gap can cause discrepancies in self-attention. Due to this gap, the transformer attends
Externí odkaz:
http://arxiv.org/abs/2211.14703
Autor:
Zheng, Yi, Gindra, Rushin H., Green, Emily J., Burks, Eric J., Betke, Margrit, Beane, Jennifer E., Kolachalama, Vijaya B.
Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when performing supervised deep learning, a WSI is divided into small patches, trained and the outcomes are aggregated to estimate disease grade. However, patch-based m
Externí odkaz:
http://arxiv.org/abs/2205.09671
While pose estimation is an important computer vision task, it requires expensive annotation and suffers from domain shift. In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a syntheti
Externí odkaz:
http://arxiv.org/abs/2204.00172
Autor:
Khan, Umair, Thompson, Russell, Li, Jason, Etter, Lauren P., Camelo, Ingrid, Pieciak, Rachel C., Castro-Aragon, Ilse, Setty, Bindu, Gill, Christopher C., Demi, Libertario, Betke, Margrit
Publikováno v:
In Computers in Biology and Medicine September 2024 180
Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data. The most common approaches try to generate images or features mimicking the distribution in th
Externí odkaz:
http://arxiv.org/abs/2009.08610