Anass BAIROUK

Autonomous driving presents a complex challenge, which is usually addressed with artificial intelligence models that are end-to-end or modular in nature. Within the landscape of modular approaches, a bio-inspired neural circuit policy model has emerged as an innovative control module, offering a compact and inherently interpretable system to infer a steering wheel command from abstract visual features. Here, we take a leap forward by integrating a variational autoencoder with the neural circuit… عرض المزيد

Autonomous driving presents a complex challenge, which is usually addressed with artificial intelligence models that are end-to-end or modular in nature. Within the landscape of modular approaches, a bio-inspired neural circuit policy model has emerged as an innovative control module, offering a compact and inherently interpretable system to infer a steering wheel command from abstract visual features. Here, we take a leap forward by integrating a variational autoencoder with the neural circuit policy controller, forming a solution that directly generates steering commands from input camera images. By substituting the traditional convolutional neural network approach to feature extraction with a variational autoencoder, we enhance the system's interpretability, enabling a more transparent and understandable decision-making process.
In addition to the architectural shift toward a variational autoencoder, this study introduces the automatic latent perturbation tool, a novel contribution designed to probe and elucidate the latent features within the variational autoencoder. The automatic latent perturbation tool automates the interpretability process, offering granular insights into how specific latent variables influence the overall model's behavior. Through a series of numerical experiments, we demonstrate the interpretative power of the variational autoencoder-neural circuit policy model and the utility of the automatic latent perturbation tool in making the inner workings of autonomous driving systems more transparent. عرض أقل

Aims. The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys
following up on transient objects are in progress or under construction, such as the Vera Rubin Observatory Legacy Survey for Space
and Time (LSST), which is poised to produce huge amounts of these time series. The associated scientific topics are extensive, ranging
from the study of objects in our galaxy to the observation of the most distant supernovae for… عرض المزيد

Aims. The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys
following up on transient objects are in progress or under construction, such as the Vera Rubin Observatory Legacy Survey for Space
and Time (LSST), which is poised to produce huge amounts of these time series. The associated scientific topics are extensive, ranging
from the study of objects in our galaxy to the observation of the most distant supernovae for measuring the expansion of the universe.
With such a large amount of data available, the need for robust automatic tools to detect and classify celestial objects is growing
steadily.
Methods. This study is based on the assumption that astronomical images contain more information than light curves. In this paper,
we propose a novel approach based on deep learning for classifying different types of space objects directly using images. We named
our approach ConvEntion, which stands for CONVolutional attENTION. It is based on convolutions and transformers, which are new
approaches for the treatment of astronomical image time series. Our solution integrates spatio-temporal features and can be applied
to various types of image datasets with any number of bands.
Results. In this work, we solved various problems the datasets tend to suffer from and we present new results for classifications using
astronomical image time series with an increase in accuracy of 13%, compared to state-of-the-art approaches that use image time
series, and a 12% increase, compared to approaches that use light curves عرض أقل

Recently, convolutional neural networks (CNNs) have been used as a powerful tool to solve many problems of machine learning and computer vision. In this article, we aim to provide insight on how using a Gabor filter to improve the performance of many CNN architectures. Specifically, existing CNN models (ResNet, AlexNet, VGG16, InceptionV3).