Zobrazeno 1 - 10
of 3 388
pro vyhledávání: '"Ning, Xia"'
Autor:
Averly, Reza, Ning, Xia
Clinical named entity recognition (NER) aims to retrieve important entities within clinical narratives. Recent works have demonstrated that large language models (LLMs) can achieve strong performance in this task. While previous works focus on propri
Externí odkaz:
http://arxiv.org/abs/2407.04629
Chemistry plays a crucial role in many domains, such as drug discovery and material science. While large language models (LLMs) such as GPT-4 exhibit remarkable capabilities on natural language processing tasks, existing research indicates that their
Externí odkaz:
http://arxiv.org/abs/2402.09391
With tremendous efforts on developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfactory performance on new users and new products - a typical out-of-doma
Externí odkaz:
http://arxiv.org/abs/2402.08831
Publikováno v:
Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao; Apr2021, Vol. 32 Issue 4, p1259-1268, 10p
Autor:
Dey, Vishal, Ning, Xia
Pretrained Graph Neural Networks have been widely adopted for various molecular property prediction tasks. Despite their ability to encode structural and relational features of molecules, traditional fine-tuning of such pretrained GNNs on the target
Externí odkaz:
http://arxiv.org/abs/2401.16299
Publikováno v:
ACM Transactions on Knowledge Discovery from Data (TKDD) 2024
Self-attention (SA) mechanisms have been widely used in developing sequential recommendation (SR) methods, and demonstrated state-of-the-art performance. However, in this paper, we show that self-attentive SR methods substantially suffer from the ove
Externí odkaz:
http://arxiv.org/abs/2311.07742
Autor:
Xiang, Shunian, Lawrence, Patrick J., Peng, Bo, Chiang, ChienWei, Kim, Dokyoon, Shen, Li, Ning, Xia
Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex net
Externí odkaz:
http://arxiv.org/abs/2310.15211
Autor:
Lawrence, Patrick J., Ning, Xia
Due to cancer's complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patients makes it difficult to quickly iden
Externí odkaz:
http://arxiv.org/abs/2310.13725
In recent years, with large language models (LLMs) achieving state-of-the-art performance in context understanding, increasing efforts have been dedicated to developing LLM-enhanced sequential recommendation (SR) methods. Considering that most existi
Externí odkaz:
http://arxiv.org/abs/2310.01612
Learning effective recommendation models from sparse user interactions represents a fundamental challenge in developing sequential recommendation methods. Recently, pre-training-based methods have been developed to tackle this challenge. Though promi
Externí odkaz:
http://arxiv.org/abs/2309.10195