Zobrazeno 1 - 10
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pro vyhledávání: '"Ayache, A."'
Research on non-verbal behavior generation for social interactive agents focuses mainly on the believability and synchronization of non-verbal cues with speech. However, existing models, predominantly based on deep learning architectures, often perpe
Externí odkaz:
http://arxiv.org/abs/2410.07274
Integrating named entity recognition (NER) with automatic speech recognition (ASR) can significantly enhance transcription accuracy and informativeness. In this paper, we introduce WhisperNER, a novel model that allows joint speech transcription and
Externí odkaz:
http://arxiv.org/abs/2409.08107
The generalized filtered method of moments was developed in the recent papers by Alomari et al., 2020, and Ayache et al., 2022. It used functional data obtained from continuously sampled cyclic long-memory stochastic processes to simultaneously estim
Externí odkaz:
http://arxiv.org/abs/2407.12444
Consider the setting of multiple random walks (RWs) on a graph executing a certain computational task. For instance, in decentralized learning via RWs, a model is updated at each iteration based on the local data of the visited node and then passed t
Externí odkaz:
http://arxiv.org/abs/2407.11762
Transformer models have recently become very successful in the natural language domain. Their value as sequence-to-sequence translators there, also makes them a highly interesting technique for learning relationships between astrophysical time series
Externí odkaz:
http://arxiv.org/abs/2406.12515
Autor:
Sicre, Ronan, Zhang, Hanwei, Dejasmin, Julien, Daaloul, Chiheb, Ayache, Stéphane, Artières, Thierry
This paper presents Discriminative Part Network (DP-Net), a deep architecture with strong interpretation capabilities, which exploits a pretrained Convolutional Neural Network (CNN) combined with a part-based recognition module. This system learns an
Externí odkaz:
http://arxiv.org/abs/2404.15037
This paper studies interpretability of convolutional networks by means of saliency maps. Most approaches based on Class Activation Maps (CAM) combine information from fully connected layers and gradient through variants of backpropagation. However, i
Externí odkaz:
http://arxiv.org/abs/2404.15024
Explanations obtained from transformer-based architectures in the form of raw attention, can be seen as a class-agnostic saliency map. Additionally, attention-based pooling serves as a form of masking the in feature space. Motivated by this observati
Externí odkaz:
http://arxiv.org/abs/2404.14996
Autor:
Ahrer, E., Seidel, J. V., Doyle, L., Gandhi, S., Prinoth, B., Cegla, H. M., McDonald, C. H., Astudillo-Defru, N., Ayache, E., Nealon, R., Veras, Dimitri, Wheatley, P. J., Ehrenreich, D.
We present high spectral resolution observations of the hot Jupiter WASP-94 A b using the HARPS instrument on ESO's 3.6m telescope in La Silla, Chile. We probed for Na absorption in its atmosphere as well as constrained the previously reported misali
Externí odkaz:
http://arxiv.org/abs/2404.06550
Autor:
Ayache, Antoine, Xiao, Yimin
Non-Gaussian Harmonizable Fractional Stable Motion (HFSM) is a natural and important extension of the well-known Fractional Brownian Motion to the framework of heavy-tailed stable distributions. It was introduced several decades ago; however its prop
Externí odkaz:
http://arxiv.org/abs/2310.04518