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pro vyhledávání: '"P. Holla"'
Understanding distinct neurological aging patterns across various populations is vital in the context of a globally aging populace. This study seeks to unravel the structural variations in the aging brain, taking into consideration different ethnic b
Externí odkaz:
http://arxiv.org/abs/2408.07280
This paper describes the architecture and systems built towards solving the SemEval 2023 Task 2: MultiCoNER II (Multilingual Complex Named Entity Recognition) [1]. We evaluate two approaches (a) a traditional Conditional Random Fields model and (b) a
Externí odkaz:
http://arxiv.org/abs/2401.00698
Autor:
Holla, Meghana, Lourentzou, Ismini
Zero-shot Natural Language-Video Localization (NLVL) methods have exhibited promising results in training NLVL models exclusively with raw video data by dynamically generating video segments and pseudo-query annotations. However, existing pseudo-quer
Externí odkaz:
http://arxiv.org/abs/2312.17429
In recent years, the development of robust Intelligent transportation systems (ITS) is tackled across the globe to provide better traffic efficiency by reducing frequent traffic problems. As an application of ITS, vehicle re-identification has gained
Externí odkaz:
http://arxiv.org/abs/2208.06579
Publikováno v:
ACL-IJCNLP 2021
A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses. This inspired recent research on few-shot WSD using meta-learning. While
Externí odkaz:
http://arxiv.org/abs/2106.02960
Autor:
Lippe, Phillip, Holla, Nithin, Chandra, Shantanu, Rajamanickam, Santhosh, Antoniou, Georgios, Shutova, Ekaterina, Yannakoudakis, Helen
Publikováno v:
PMLR 133:344-360, 2021
An increasingly common expression of online hate speech is multimodal in nature and comes in the form of memes. Designing systems to automatically detect hateful content is of paramount importance if we are to mitigate its undesirable effects on the
Externí odkaz:
http://arxiv.org/abs/2012.12871
Lifelong learning requires models that can continuously learn from sequential streams of data without suffering catastrophic forgetting due to shifts in data distributions. Deep learning models have thrived in the non-sequential learning paradigm; ho
Externí odkaz:
http://arxiv.org/abs/2009.04891
The success of deep learning methods hinges on the availability of large training datasets annotated for the task of interest. In contrast to human intelligence, these methods lack versatility and struggle to learn and adapt quickly to new tasks, whe
Externí odkaz:
http://arxiv.org/abs/2004.14355
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