Zobrazeno 1 - 10
of 1 481
pro vyhledávání: '"Webb, A. I."'
Autor:
Zheng, Yizhen, Koh, Huan Yee, Yang, Maddie, Li, Li, May, Lauren T., Webb, Geoffrey I., Pan, Shirui, Church, George
The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding disease mechanisms, facilitating drug discovery, and optimizing clinical
Externí odkaz:
http://arxiv.org/abs/2409.04481
Unsupervised graph representation learning (UGRL) based on graph neural networks (GNNs), has received increasing attention owing to its efficacy in handling graph-structured data. However, existing UGRL methods ideally assume that the node features a
Externí odkaz:
http://arxiv.org/abs/2407.19944
Autor:
Darban, Zahra Zamanzadeh, Yang, Yiyuan, Webb, Geoffrey I., Aggarwal, Charu C., Wen, Qingsong, Salehi, Mahsa
In time series anomaly detection (TSAD), the scarcity of labeled data poses a challenge to the development of accurate models. Unsupervised domain adaptation (UDA) offers a solution by leveraging labeled data from a related domain to detect anomalies
Externí odkaz:
http://arxiv.org/abs/2404.11269
Earth observation (EO) satellite missions have been providing detailed images about the state of the Earth and its land cover for over 50 years. Long term missions, such as NASA's Landsat, Terra, and Aqua satellites, and more recently, the ESA's Sent
Externí odkaz:
http://arxiv.org/abs/2404.03936
GraphRT is a graph based deep learning model that predicts the retention time (RT) of peptides in liquid chromatography tandem mass spectrometry (LC MSMS) experiments. Each amino acid is represented as a graph, capturing its atomic and structural pro
Externí odkaz:
http://arxiv.org/abs/2402.02661
Logistic regression is a ubiquitous method for probabilistic classification. However, the effectiveness of logistic regression depends upon careful and relatively computationally expensive tuning, especially for the regularisation hyperparameter, and
Externí odkaz:
http://arxiv.org/abs/2401.15610
Autor:
Foumani, Navid Mohammadi, Tan, Chang Wei, Webb, Geoffrey I., Rezatofighi, Hamid, Salehi, Mahsa
We argue that time series analysis is fundamentally different in nature to either vision or natural language processing with respect to the forms of meaningful self-supervised learning tasks that can be defined. Motivated by this insight, we introduc
Externí odkaz:
http://arxiv.org/abs/2312.03998
The ability to compute the exact divergence between two high-dimensional distributions is useful in many applications but doing so naively is intractable. Computing the alpha-beta divergence -- a family of divergences that includes the Kullback-Leibl
Externí odkaz:
http://arxiv.org/abs/2310.09129
Autor:
Zheng, Yizhen, Koh, Huan Yee, Ju, Jiaxin, Nguyen, Anh T. N., May, Lauren T., Webb, Geoffrey I., Pan, Shirui
Large language models are a form of artificial intelligence systems whose primary knowledge consists of the statistical patterns, semantic relationships, and syntactical structures of language1. Despite their limited forms of "knowledge", these syste
Externí odkaz:
http://arxiv.org/abs/2310.07984
Autor:
Ismail-Fawaz, Ali, Fawaz, Hassan Ismail, Petitjean, François, Devanne, Maxime, Weber, Jonathan, Berretti, Stefano, Webb, Geoffrey I., Forestier, Germain
Time series data can be found in almost every domain, ranging from the medical field to manufacturing and wireless communication. Generating realistic and useful exemplars and prototypes is a fundamental data analysis task. In this paper, we investig
Externí odkaz:
http://arxiv.org/abs/2309.16353