2 citations · 4 across the 6 of their papers we have counts for
6 papers
Contrastive Learning of Asset Embeddings from Financial Time Series
Rian Dolphin, Barry Smyth, Ruihai Dong
Representation learning has emerged as a powerful paradigm for extracting valuable latent features from complex, high-dimensional data. In financial domains, learning informative r…
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation
Siteng Ma, Haochang Wu, Aonghus Lawlor +1
Active learning (AL) has found wide applications in medical image segmentation, aiming to alleviate the annotation workload and enhance performance. Conventional uncertainty-based…
Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases
Haiwen Du, Zheng Ju, Yu An +5
Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollut…
Enhancing Topic Extraction in Recommender Systems with Entropy Regularization
Xuefei Jiang, Dairui Liu, Ruihai Dong
In recent years, many recommender systems have utilized textual data for topic extraction to enhance interpretability. However, our findings reveal a noticeable deficiency in the c…
Learning to Generalize for Cross-domain QA
Yingjie Niu, Linyi Yang, Ruihai Dong +1
There have been growing concerns regarding the out-of-domain generalization ability of natural language processing (NLP) models, particularly in question-answering (QA) tasks. Curr…
Industry Classification Using a Novel Financial Time-Series Case Representation
Rian Dolphin, Barry Smyth, Ruihai Dong
The financial domain has proven to be a fertile source of challenging machine learning problems across a variety of tasks including prediction, clustering, and classification. Rese…