arXiv subjects

Fatemeh Sadat Masoumi

Publications and source records attributed to Fatemeh Sadat Masoumi.

4 recordsLinked to original sources

arXiv2025

Boosting Hierarchical Reinforcement Learning with Meta-Learning for Complex Task Adaptation

Hierarchical Reinforcement Learning (HRL) is well-suitedd for solving complex tasks by breaking them down into structured policies. However, HRL agents often struggle with efficient exploration and quick adaptation. To overcome these limitations, we propose integrating meta-learning into HRL to enable agents to learn and adapt hierarchical policies more effectively. Our method leverages meta-learning to facilitate rapid task adaptation using prior experience, while intrinsic motivation mechanisms drive efficient exploration by rewarding the discovery of novel states. Specifically, our agent employs a high-level policy to choose among multiple low-level policies within custom-designed grid environments. By incorporating gradient-based meta-learning with differentiable inner-loop updates, we optimize performance across a curriculum of progressively challenging tasks. Experimental results highlight that our metalearning-enhanced hierarchical agent significantly outperforms standard HRL approaches lacking meta-learning and intrinsic motivation. The agent demonstrates faster learning, greater cumulative rewards, and higher success rates in complex grid-based scenarios. These Findings underscore the effectiveness of combining meta-learning, curriculum learning, and intrinsic motivation to enhance the capability of HRL agents in tackling complex tasks.

cs.LG↗

arXiv2023

Augmenting Vision-Based Human Pose Estimation with Rotation Matrix

Fitness applications are commonly used to monitor activities within the gym, but they often fail to automatically track indoor activities inside the gym. This study proposes a model that utilizes pose estimation combined with a novel data augmentation method, i.e., rotation matrix. We aim to enhance the classification accuracy of activity recognition based on pose estimation data. Through our experiments, we experiment with different classification algorithms along with image augmentation approaches. Our findings demonstrate that the SVM with SGD optimization, using data augmentation with the Rotation Matrix, yields the most accurate results, achieving a 96% accuracy rate in classifying five physical activities. Conversely, without implementing the data augmentation techniques, the baseline accuracy remains at a modest 64%.

cs.CV↗

arXiv2023

A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian Reviews

Aspect-based sentiment analysis is of great importance and application because of its ability to identify all aspects discussed in the text. However, aspect-based sentiment analysis will be most effective when, in addition to identifying all the aspects discussed in the text, it can also identify their polarity. Most previous methods use the pipeline approach, that is, they first identify the aspects and then identify the polarities. Such methods are unsuitable for practical applications since they can lead to model errors. Therefore, in this study, we propose a multi-task learning model based on Convolutional Neural Networks (CNNs), which can simultaneously detect aspect category and detect aspect category polarity. creating a model alone may not provide the best predictions and lead to errors such as bias and high variance. To reduce these errors and improve the efficiency of model predictions, combining several models known as ensemble learning may provide better results. Therefore, the main purpose of this article is to create a model based on an ensemble of multi-task deep convolutional neural networks to enhance sentiment analysis in Persian reviews. We evaluated the proposed method using a Persian language dataset in the movie domain. Jacquard index and Hamming loss measures were used to evaluate the performance of the developed models. The results indicate that this new approach increases the efficiency of the sentiment analysis model in the Persian language.

cs.CL↗

arXiv2022

Utilizing distilBert transformer model for sentiment classification of COVID-19's Persian open-text responses

The COVID-19 pandemic has caused drastic alternations in human life in all aspects. The government's laws in this regard affected the lifestyle of all people. Due to this fact studying the sentiment of individuals is essential to be aware of the future impacts of the coming pandemics. To contribute to this aim, we proposed an NLP (Natural Language Processing) model to analyze open-text answers in a survey in Persian and detect positive and negative feelings of the people in Iran. In this study, a distilBert transformer model was applied to take on this task. We deployed three approaches to perform the comparison, and our best model could gain accuracy: 0.824, Precision: 0.824, Recall: 0.798, and F1 score: 0.804.

cs.CL↗