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pro vyhledávání: '"Ghosal, Soumya Suvra"'
Image-text contrastive models such as CLIP learn transferable and robust representations for zero-shot transfer to a variety of downstream tasks. However, to obtain strong downstream performances, prompts need to be carefully curated, which can be a
Externí odkaz:
http://arxiv.org/abs/2406.13683
Autor:
Chakraborty, Souradip, Ghosal, Soumya Suvra, Yin, Ming, Manocha, Dinesh, Wang, Mengdi, Bedi, Amrit Singh, Huang, Furong
Aligning foundation models is essential for their safe and trustworthy deployment. However, traditional fine-tuning methods are computationally intensive and require updating billions of model parameters. A promising alternative, alignment via decodi
Externí odkaz:
http://arxiv.org/abs/2405.20495
Machine learning models deployed in the wild can be challenged by out-of-distribution (OOD) data from unknown classes. Recent advances in OOD detection rely on distance measures to distinguish samples that are relatively far away from the in-distribu
Externí odkaz:
http://arxiv.org/abs/2312.14452
Autor:
Ghosal, Soumya Suvra, Chakraborty, Souradip, Geiping, Jonas, Huang, Furong, Manocha, Dinesh, Bedi, Amrit Singh
Large Language Models (LLMs) have revolutionized the domain of natural language processing (NLP) with remarkable capabilities of generating human-like text responses. However, despite these advancements, several works in the existing literature have
Externí odkaz:
http://arxiv.org/abs/2310.15264
Autor:
Ghosal, Soumya Suvra, Li, Yixuan
Modern machine learning models may be susceptible to learning spurious correlations that hold on average but not for the atypical group of samples. To address the problem, previous approaches minimize the empirical worst-group risk. Despite the promi
Externí odkaz:
http://arxiv.org/abs/2303.05809
Deep neural networks may be susceptible to learning spurious correlations that hold on average but not in atypical test samples. As with the recent emergence of vision transformer (ViT) models, it remains underexplored how spurious correlations are m
Externí odkaz:
http://arxiv.org/abs/2203.09125
Disinformation is often presented in long textual articles, especially when it relates to domains such as health, often seen in relation to COVID-19. These articles are typically observed to have a number of trustworthy sentences among which core dis
Externí odkaz:
http://arxiv.org/abs/2010.10836
Autor:
Ghosal, Soumya Suvra1 (AUTHOR) sghosal@cs.wisc.edu, Li, Yixuan1 (AUTHOR)
Publikováno v:
International Journal of Computer Vision. Mar2024, Vol. 132 Issue 3, p689-709. 21p.
Akademický článek
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Autor:
Ghosal, Soumya Suvra
Publikováno v:
ACM International Conference Proceeding Series; 1/3/2019, p314-317, 4p