Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Adler, Amir"'
Automatic hate speech detection using deep neural models is hampered by the scarcity of labeled datasets, leading to poor generalization. To mitigate this problem, generative AI has been utilized to generate large amounts of synthetic hate speech seq
Externí odkaz:
http://arxiv.org/abs/2311.09993
We consider the problem of 3D seismic inversion from pre-stack data using a very small number of seismic sources. The proposed solution is based on a combination of compressed-sensing and machine learning frameworks, known as compressed-learning. The
Externí odkaz:
http://arxiv.org/abs/2311.00107
Publikováno v:
Frontiers in Human Neuroscience, 2023
Magnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio
Externí odkaz:
http://arxiv.org/abs/2306.05892
Inverting seismic data to build 3D geological structures is a challenging task due to the overwhelming amount of acquired seismic data, and the very-high computational load due to iterative numerical solutions of the wave equation, as required by ind
Externí odkaz:
http://arxiv.org/abs/2207.14789
We present a novel solution to the problem of localizing magnetoencephalography (MEG) and electroencephalography (EEG) brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares criterion by the Alternating
Externí odkaz:
http://arxiv.org/abs/2202.01120
The massive spread of hate speech, hateful content targeted at specific subpopulations, is a problem of critical social importance. Automated methods of hate speech detection typically employ state-of-the-art deep learning (DL)-based text classifiers
Externí odkaz:
http://arxiv.org/abs/2111.06336
Automatic hate speech detection is hampered by the scarcity of labeled datasetd, leading to poor generalization. We employ pretrained language models (LMs) to alleviate this data bottleneck. We utilize the GPT LM for generating large amounts of synth
Externí odkaz:
http://arxiv.org/abs/2109.00591
Autor:
Wax, Mati, Adler, Amir
Publikováno v:
In Signal Processing July 2024 220
Publikováno v:
In Biomedical Signal Processing and Control April 2024 90
Autor:
Pantazis, Dimitrios, Adler, Amir
We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned for single and multiple time point MEG data, and can estimate varying numbers of dipole
Externí odkaz:
http://arxiv.org/abs/2012.00588