Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Van Linh, Ngo"'
Continual Event Detection (CED) poses a formidable challenge due to the catastrophic forgetting phenomenon, where learning new tasks (with new coming event types) hampers performance on previous ones. In this paper, we introduce a novel approach, Lif
Externí odkaz:
http://arxiv.org/abs/2410.08905
Autor:
Pham, Duy-Tung, Vu, Thien Trang Nguyen, Nguyen, Tung, Van, Linh Ngo, Nguyen, Duc Anh, Nguyen, Thien Huu
Recent advances in neural topic models have concentrated on two primary directions: the integration of the inference network (encoder) with a pre-trained language model (PLM) and the modeling of the relationship between words and topics in the genera
Externí odkaz:
http://arxiv.org/abs/2409.19749
Autor:
Luong, Tinh Son, Le, Thanh-Thien, Doan, Thang Viet, Van, Linh Ngo, Nguyen, Thien Huu, Nguyen, Diep Thi-Ngoc
Existing toxic detection models face significant limitations, such as lack of transparency, customization, and reproducibility. These challenges stem from the closed-source nature of their training data and the paucity of explanations for their evalu
Externí odkaz:
http://arxiv.org/abs/2406.14835
Large language models (LLMs) have become integral to our professional workflows and daily lives. Nevertheless, these machine companions of ours have a critical flaw: the huge amount of data which endows them with vast and diverse knowledge, also expo
Externí odkaz:
http://arxiv.org/abs/2405.10659
In this work, we examine the advantages of using multiple types of behaviour in recommendation systems. Intuitively, each user has to do some implicit actions (e.g., click) before making an explicit decision (e.g., purchase). Previous studies showed
Externí odkaz:
http://arxiv.org/abs/2107.12325
Analyzing texts from social media encounters many challenges due to their unique characteristics of shortness, massiveness, and dynamic. Short texts do not provide enough context information, causing the failure of the traditional statistical models.
Externí odkaz:
http://arxiv.org/abs/2003.11948
Learning hidden topics from data streams has become absolutely necessary but posed challenging problems such as concept drift as well as short and noisy data. Using prior knowledge to enrich a topic model is one of potential solutions to cope with th
Externí odkaz:
http://arxiv.org/abs/2003.06112
We consider how to effectively use prior knowledge when learning a Bayesian model from streaming environments where the data come infinitely and sequentially. This problem is highly important in the era of data explosion and rich sources of precious
Externí odkaz:
http://arxiv.org/abs/2003.06123
Publikováno v:
In Neurocomputing 21 September 2022 505:30-43
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.