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Author : Gaurav Sahu, Olga Vechtomova, Issam H. Laradji

Abstract : This work tackles the task of extractive text summarization in a limited labeled data scenario using a semi-supervised approach. Specifically, we propose a prompt-based pseudolabel selection strategy using GPT-4. We evaluate our method on three text summarization datasets: TweetSumm, WikiHow, and ArXiv/PubMed. Our experiments show that by using an LLM to evaluate and generate pseudolabels, we can improve the ROUGE-1 by 1020% on the different datasets, which is akin to enhancing pretrained models. We also show that such a method needs a smaller pool of unlabeled examples to perform better

2.Semi-supervised machine learning model for Lagrangian flow state estimation (arXiv)

Author : Reno Miura, Koji Fukagata

Abstract : In recent years, many researchers have demonstrated the strength of supervised machine learning for flow state estimation. Most of the studies assume that the sensors are fixed and the high-resolution ground truth can be prepared. However, the sensors are not always fixed and may be floating in practical situations for example, in oceanography and river hydraulics, sensors are generally floating. In addition, floating sensors make it more difficult to collect the high-resolution ground truth. We here propose a machine learning model for state estimation from such floating sensors without requiring high-resolution ground-truth data for training. This model estimates velocity fields only from floating sensor measurements and is trained with a loss function using only sensor locations. We call this loss function as a semi-supervised loss function, since the sensor measurements are utilized as the ground truth but high-resolution data of the entire velocity fields are not required. To demonstrate the performance of the proposed model, we consider Stokes second problem and two-dimensional decaying homogeneous isotropic turbulence. Our results reveal that the proposed semi-supervised model can estimate velocity fields with reasonable accuracy when the appropriate number of sensors are spatially distributed to some extent in the domain. We also discuss the dependence of the estimation accuracy on the number and distribution of sensors.

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Applications of Semi-supervised Learning part4(Machine Learning ... - Medium