Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Tran, Quan Hung"'
Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models
Autor:
Nguyen, Minh, Dernoncourt, Franck, Yoon, Seunghyun, Deilamsalehy, Hanieh, Tan, Hao, Rossi, Ryan, Tran, Quan Hung, Bui, Trung, Nguyen, Thien Huu
We introduce an approach to identifying speaker names in dialogue transcripts, a crucial task for enhancing content accessibility and searchability in digital media archives. Despite the advancements in speech recognition, the task of text-based spea
Externí odkaz:
http://arxiv.org/abs/2407.12094
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. In this paper, we introduce a novel approach called class-aware optimal transport (OT), which measures the OT distance between
Externí odkaz:
http://arxiv.org/abs/2401.15952
Autor:
Deng, Zhongfen, Yoon, Seunghyun, Bui, Trung, Dernoncourt, Franck, Tran, Quan Hung, Liu, Shuaiqi, Zhao, Wenting, Zhang, Tao, Wang, Yibo, Yu, Philip S.
Aspect-based meeting transcript summarization aims to produce multiple summaries, each focusing on one aspect of content in a meeting transcript. It is challenging as sentences related to different aspects can mingle together, and those relevant to a
Externí odkaz:
http://arxiv.org/abs/2311.04292
Data-Free Knowledge Distillation (DFKD) has made significant recent strides by transferring knowledge from a teacher neural network to a student neural network without accessing the original data. Nonetheless, existing approaches encounter a signific
Externí odkaz:
http://arxiv.org/abs/2310.00258
Autor:
Li, Zhuang, Chai, Yuyang, Zhuo, Terry Yue, Qu, Lizhen, Haffari, Gholamreza, Li, Fei, Ji, Donghong, Tran, Quan Hung
Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval. However, existing scene graph parsers that convert image captions into scene graphs often s
Externí odkaz:
http://arxiv.org/abs/2305.17497
Autor:
Nguyen-Duc, Thang, Thanh-Tung, Hoang, Tran, Quan Hung, Huu-Tien, Dang, Nguyen, Hieu Ngoc, Dau, Anh T. V., Bui, Nghi D. Q.
Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets. However, they are unstable when applied to deep networks. In this paper, we provide an explanation for the instability of IFs and develop a soluti
Externí odkaz:
http://arxiv.org/abs/2305.01384
Autor:
Nguyen, Van-Anh, Dinh, Khanh Pham, Vuong, Long Tung, Do, Thanh-Toan, Tran, Quan Hung, Phung, Dinh, Le, Trung
Recently vision transformers (ViT) have been applied successfully for various tasks in computer vision. However, important questions such as why they work or how they behave still remain largely unknown. In this paper, we propose an effective visuali
Externí odkaz:
http://arxiv.org/abs/2210.07646
Autor:
Vo, Vy, Nguyen, Van, Le, Trung, Tran, Quan Hung, Haffari, Gholamreza, Camtepe, Seyit, Phung, Dinh
Publikováno v:
In The Eleventh International Conference on Learning Representations, 2023
Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. A large number of interpreting methods focus on identifying explanatory input features, which generally fall into two main categories:
Externí odkaz:
http://arxiv.org/abs/2207.03113
Identifying vulnerabilities in the source code is essential to protect the software systems from cyber security attacks. It, however, is also a challenging step that requires specialized expertise in security and code representation. To this end, we
Externí odkaz:
http://arxiv.org/abs/2110.07317
Autor:
Zhang, Jianguo, Bui, Trung, Yoon, Seunghyun, Chen, Xiang, Liu, Zhiwei, Xia, Congying, Tran, Quan Hung, Chang, Walter, Yu, Philip
In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-shot intent detection schema via contrastive pre-training and fine-tun
Externí odkaz:
http://arxiv.org/abs/2109.06349