Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Boubrahimi, Soukaina Filali"'
M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps
Over the past decade, multivariate time series classification has received great attention. Machine learning (ML) models for multivariate time series classification have made significant strides and achieved impressive success in a wide range of appl
Externí odkaz:
http://arxiv.org/abs/2411.02649
Current Generative Adversarial Network (GAN)-based approaches for time series generation face challenges such as suboptimal convergence, information loss in embedding spaces, and instability. To overcome these challenges, we introduce an advanced fra
Externí odkaz:
http://arxiv.org/abs/2410.21203
Autor:
Li, Peiyu, Bahri, Omar, Hosseinzadeh, Pouya, Boubrahimi, Soukaïna Filali, Hamdi, Shah Muhammad
As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for building trust and transparency in
Externí odkaz:
http://arxiv.org/abs/2410.20539
RF-GAP has recently been introduced as an improved random forest proximity measure. In this paper, we present PF-GAP, an extension of RF-GAP proximities to proximity forests, an accurate and efficient time series classification model. We use the fore
Externí odkaz:
http://arxiv.org/abs/2410.03098
Major solar flares are abrupt surges in the Sun's magnetic flux, presenting significant risks to technological infrastructure. In view of this, effectively predicting major flares from solar active region magnetic field data through machine learning
Externí odkaz:
http://arxiv.org/abs/2410.00312
Accurate solar flare prediction is crucial due to the significant risks that intense solar flares pose to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by utilizing advanced data prepro
Externí odkaz:
http://arxiv.org/abs/2409.14016
Generating time series data using Generative Adversarial Networks (GANs) presents several prevalent challenges, such as slow convergence, information loss in embedding spaces, instability, and performance variability depending on the series length. T
Externí odkaz:
http://arxiv.org/abs/2409.14013
With the rising need of interpretable machine learning methods, there is a necessity for a rise in human effort to provide diverse explanations of the influencing factors of the model decisions. To improve the trust and transparency of AI-based syste
Externí odkaz:
http://arxiv.org/abs/2211.04411
As machine learning and deep learning models have become highly prevalent in a multitude of domains, the main reservation in their adoption for decision-making processes is their black-box nature. The Explainable Artificial Intelligence (XAI) paradig
Externí odkaz:
http://arxiv.org/abs/2208.10462
Autor:
Nita, Gelu, Georgoulis, Manolis, Kitiashvili, Irina, Sadykov, Viacheslav, Camporeale, Enrico, Kosovichev, Alexander, Wang, Haimin, Oria, Vincent, Wang, Jason, Angryk, Rafal, Aydin, Berkay, Ahmadzadeh, Azim, Bai, Xiaoli, Bastian, Timothy, Boubrahimi, Soukaina Filali, Chen, Bin, Davey, Alisdair, Fereira, Sheldon, Fleishman, Gregory, Gary, Dale, Gerrard, Andrew, Hellbourg, Gregory, Herbert, Katherine, Ireland, Jack, Illarionov, Egor, Kuroda, Natsuha, Li, Qin, Liu, Chang, Liu, Yuexin, Kim, Hyomin, Kempton, Dustin, Ma, Ruizhe, Martens, Petrus, McGranaghan, Ryan, Semones, Edward, Stefan, John, Stejko, Andrey, Collado-Vega, Yaireska, Wang, Meiqi, Xu, Yan, Yu, Sijie
The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. The
Externí odkaz:
http://arxiv.org/abs/2006.12224