Zobrazeno 1 - 10
of 24 573
pro vyhledávání: '"LIU, Fei"'
Countless decisions shape our daily lives, and it is paramount to understand the how and why behind these choices. In this paper, we introduce a new LLM decision-making framework called STRUX, which enhances LLM decision-making by providing structure
Externí odkaz:
http://arxiv.org/abs/2410.12583
Symbolic regression (SR) methods have been extensively investigated to explore explicit algebraic Reynolds stress models (EARSM) for turbulence closure of Reynolds-averaged Navier-Stokes (RANS) equations. The deduced EARSM can be readily implemented
Externí odkaz:
http://arxiv.org/abs/2410.10657
Graph Convolutional Networks (GCNs) are widely used in graph-based applications, such as social networks and recommendation systems. Nevertheless, large-scale graphs or deep aggregation layers in full-batch GCNs consume significant GPU memory, causin
Externí odkaz:
http://arxiv.org/abs/2410.10089
This paper proposes a novel Stage-wise and Prior-aware Neural Speech Phase Prediction (SP-NSPP) model, which predicts the phase spectrum from input amplitude spectrum by two-stage neural networks. In the initial prior-construction stage, we prelimina
Externí odkaz:
http://arxiv.org/abs/2410.04990
Autor:
Hu, Yebowen, Wang, Xiaoyang, Yao, Wenlin, Lu, Yiming, Zhang, Daoan, Foroosh, Hassan, Yu, Dong, Liu, Fei
LLMs are ideal for decision-making due to their ability to reason over long contexts and identify critical factors. However, challenges arise when processing transcripts of spoken speech describing complex scenarios. These transcripts often contain u
Externí odkaz:
http://arxiv.org/abs/2410.01772
Autor:
Ye, Senmao, Liu, Fei
Text-to-image generation requires large amount of training data to synthesizing high-quality images. For augmenting training data, previous methods rely on data interpolations like cropping, flipping, and mixing up, which fail to introduce new inform
Externí odkaz:
http://arxiv.org/abs/2410.01638
Heuristics are commonly used to tackle diverse search and optimization problems. Design heuristics usually require tedious manual crafting with domain knowledge. Recent works have incorporated large language models (LLMs) into automatic heuristic sea
Externí odkaz:
http://arxiv.org/abs/2409.16867
Surgical automation has the capability to improve the consistency of patient outcomes and broaden access to advanced surgical care in underprivileged communities. Shared autonomy, where the robot automates routine subtasks while the surgeon retains p
Externí odkaz:
http://arxiv.org/abs/2409.14287
Chronic wounds, including diabetic ulcers, pressure ulcers, and ulcers secondary to venous hypertension, affects more than 6.5 million patients and a yearly cost of more than $25 billion in the United States alone. Chronic wound treatment is currentl
Externí odkaz:
http://arxiv.org/abs/2409.14282
Learning user preferences from implicit feedback is one of the core challenges in recommendation. The difficulty lies in the potential noise within implicit feedback. Therefore, various denoising recommendation methods have been proposed recently. Ho
Externí odkaz:
http://arxiv.org/abs/2409.12730