Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Miramond, Benoît"'
Interest in spiking neural networks (SNNs) has been growing steadily, promising an energy-efficient alternative to formal neural networks (FNNs), commonly known as artificial neural networks (ANNs). Despite increasing interest, especially for Edge ap
Externí odkaz:
http://arxiv.org/abs/2406.18350
Autor:
Courtois, Jonathan, Novac, Pierre-Emmanuel, Lemaire, Edgar, Pegatoquet, Alain, Miramond, Benoit
The complexity of event-based object detection (OD) poses considerable challenges. Spiking Neural Networks (SNNs) show promising results and pave the way for efficient event-based OD. Despite this success, the path to efficient SNNs on embedded devic
Externí odkaz:
http://arxiv.org/abs/2406.17617
Recent progress in the fields of AI and cognitive sciences opens up new challenges that were previously inaccessible to study. One of such modern tasks is recovering lost data of one modality by using the data from another one. A similar effect (call
Externí odkaz:
http://arxiv.org/abs/2307.15095
In recent years, Deep Convolutional Neural Networks (DCNNs) have outreached the performance of classical algorithms for image restoration tasks. However most of these methods are not suited for computational efficiency and are therefore too expensive
Externí odkaz:
http://arxiv.org/abs/2305.11898
Autor:
Lemaire, Edgar, Cordone, Loic, Castagnetti, Andrea, Novac, Pierre-Emmanuel, Courtois, Jonathan, Miramond, Benoit
Spiking Neural Networks are a type of neural networks where neurons communicate using only spikes. They are often presented as a low-power alternative to classical neural networks, but few works have proven these claims to be true. In this work, we p
Externí odkaz:
http://arxiv.org/abs/2210.13107
Automotive embedded algorithms have very high constraints in terms of latency, accuracy and power consumption. In this work, we propose to train spiking neural networks (SNNs) directly on data coming from event cameras to design fast and efficient au
Externí odkaz:
http://arxiv.org/abs/2205.04339
Autor:
Muliukov, Artem R., Rodriguez, Laurent, Miramond, Benoit, Khacef, Lyes, Schmidt, Joachim, Berthet, Quentin, Upegui, Andres
The field of artificial intelligence has significantly advanced over the past decades, inspired by discoveries from the fields of biology and neuroscience. The idea of this work is inspired by the process of self-organization of cortical areas in the
Externí odkaz:
http://arxiv.org/abs/2201.02262
Autor:
Novac, Pierre-Emmanuel, Hacene, Ghouthi Boukli, Pegatoquet, Alain, Miramond, Benoît, Gripon, Vincent
Publikováno v:
Sensors 2021, 21, 2984
Embedding Artificial Intelligence onto low-power devices is a challenging task that has been partly overcome with recent advances in machine learning and hardware design. Presently, deep neural networks can be deployed on embedded targets to perform
Externí odkaz:
http://arxiv.org/abs/2105.13331
Convolutional neural networks (CNNs) are now the de facto solution for computer vision problems thanks to their impressive results and ease of learning. These networks are composed of layers of connected units called artificial neurons, loosely model
Externí odkaz:
http://arxiv.org/abs/2104.12579
Few-shot classification is a challenge in machine learning where the goal is to train a classifier using a very limited number of labeled examples. This scenario is likely to occur frequently in real life, for example when data acquisition or labelin
Externí odkaz:
http://arxiv.org/abs/2009.03665