Zobrazeno 1 - 10
of 1 185
pro vyhledávání: '"Wang, QingYun"'
In the rapidly evolving field of metabolic engineering, the quest for efficient and precise gene target identification for metabolite production enhancement presents significant challenges. Traditional approaches, whether knowledge-based or model-bas
Externí odkaz:
http://arxiv.org/abs/2410.18475
Autor:
Li, Sha, Reddy, Revanth Gangi, Nguyen, Khanh Duy, Wang, Qingyun, Fung, May, Han, Chi, Han, Jiawei, Natarajan, Kartik, Voss, Clare R., Ji, Heng
Complex news events, such as natural disasters and socio-political conflicts, require swift responses from the government and society. Relying on historical events to project the future is insufficient as such events are sparse and do not cover all p
Externí odkaz:
http://arxiv.org/abs/2410.18935
Mental health disorders are one of the most serious diseases in the world. Most people with such a disease lack access to adequate care, which highlights the importance of training models for the diagnosis and treatment of mental health disorders. Ho
Externí odkaz:
http://arxiv.org/abs/2410.06845
While Vision-Language Models (VLMs) have shown remarkable abilities in visual and language reasoning tasks, they invariably generate flawed responses. Self-correction that instructs models to refine their outputs presents a promising solution to this
Externí odkaz:
http://arxiv.org/abs/2410.04055
This paper investigates data-driven cooperative output regulation for continuous-time multi-agent systems with unknown network topology. Unlike existing studies that typically assume a known network topology to directly compute controller parameters,
Externí odkaz:
http://arxiv.org/abs/2409.12824
GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation
Pose skeleton images are an important reference in pose-controllable image generation. In order to enrich the source of skeleton images, recent works have investigated the generation of pose skeletons based on natural language. These methods are base
Externí odkaz:
http://arxiv.org/abs/2409.11689
Machine learning research, crucial for technological advancements and innovation, often faces significant challenges due to its inherent complexity, slow pace of experimentation, and the necessity for specialized expertise. Motivated by this, we pres
Externí odkaz:
http://arxiv.org/abs/2408.14033
To address the challenge of automating knowledge discovery from a vast volume of literature, in this paper, we introduce a novel framework based on large language models (LLMs) that combines a progressive ontology prompting (POP) algorithm with a dua
Externí odkaz:
http://arxiv.org/abs/2409.00054
Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molecule-language pair datasets. At this point, datasets have been released
Externí odkaz:
http://arxiv.org/abs/2403.00791
Fine-grained few-shot entity extraction in the chemical domain faces two unique challenges. First, compared with entity extraction tasks in the general domain, sentences from chemical papers usually contain more entities. Moreover, entity extraction
Externí odkaz:
http://arxiv.org/abs/2401.10189