Zobrazeno 1 - 10
of 4 352
pro vyhledávání: '"Shah, Muhammad A"'
In heliophysics research, predicting solar flares is crucial due to their potential to impact both space-based systems and Earth's infrastructure substantially. Magnetic field data from solar active regions, recorded by solar imaging observatories, a
Externí odkaz:
http://arxiv.org/abs/2411.11249
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
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
Autor:
Shah, Muhammad A., Raj, Bhiksha
Automatic Speech Recognition (ASR) systems must be robust to the myriad types of noises present in real-world environments including environmental noise, room impulse response, special effects as well as attacks by malicious actors (adversarial attac
Externí odkaz:
http://arxiv.org/abs/2409.16399
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
Autor:
Shah, Muhammad A., Noguero, David Solans, Heikkila, Mikko A., Raj, Bhiksha, Kourtellis, Nicolas
As Automatic Speech Recognition (ASR) models become ever more pervasive, it is important to ensure that they make reliable predictions under corruptions present in the physical and digital world. We propose Speech Robust Bench (SRB), a comprehensive
Externí odkaz:
http://arxiv.org/abs/2403.07937
Autor:
Shah, Muhammad Ahmed, Sharma, Roshan, Dhamyal, Hira, Olivier, Raphael, Shah, Ankit, Konan, Joseph, Alharthi, Dareen, Bukhari, Hazim T, Baali, Massa, Deshmukh, Soham, Kuhlmann, Michael, Raj, Bhiksha, Singh, Rita
It has been shown that Large Language Model (LLM) alignments can be circumvented by appending specially crafted attack suffixes with harmful queries to elicit harmful responses. To conduct attacks against private target models whose characterization
Externí odkaz:
http://arxiv.org/abs/2310.04445