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pro vyhledávání: '"Xu, Mingxue"'
Matrix and tensor-guided parametrization for Natural Language Processing (NLP) models is fundamentally useful for the improvement of the model's systematic efficiency. However, the internal links between these two algebra structures and language mode
Externí odkaz:
http://arxiv.org/abs/2410.03040
Autor:
Konstantinidis, Thanos, Iacovides, Giorgos, Xu, Mingxue, Constantinides, Tony G., Mandic, Danilo
There are multiple sources of financial news online which influence market movements and trader's decisions. This highlights the need for accurate sentiment analysis, in addition to having appropriate algorithmic trading techniques, to arrive at bett
Externí odkaz:
http://arxiv.org/abs/2403.12285
High-dimensional token embeddings underpin Large Language Models (LLMs), as they can capture subtle semantic information and significantly enhance the modelling of complex language patterns. However, this high dimensionality also introduces considera
Externí odkaz:
http://arxiv.org/abs/2307.00526
Machine Learning as a Service (MLaaS) is a popular cloud-based solution for customers who aim to use an ML model but lack training data, computation resources, or expertise in ML. In this case, the training datasets are typically a private possession
Externí odkaz:
http://arxiv.org/abs/2305.09058
Autor:
Xu, Mingxue, Li, Xiang-Yang
It is a growing direction to utilize unintended memorization in ML models to benefit real-world applications, with recent efforts like user auditing, dataset ownership inference and forgotten data measurement. Standing on the point of ML model develo
Externí odkaz:
http://arxiv.org/abs/2211.13416
Publikováno v:
In Journal of Building Engineering 1 April 2024 82
Autor:
Xu, Mingxue, Fan, Chenli, Yang, Chen, Song, Kaixin, Hussain, Fayaz, Sheng, Weiqing, Wu, Jun, Wang, Huanping, Su, Weitao, Huang, Qingming, Sun, Shikuan
Publikováno v:
In Journal of Luminescence September 2021 237
Publikováno v:
In Urban Forestry & Urban Greening June 2020 52
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High-dimensional token embeddings underpin Large Language Models (LLMs), as they can capture subtle semantic information and significantly enhance the modelling of complex language patterns. However, the associated high dimensionality also introduces
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e09c37a0450a5511587a3b39e21cd40c