Zobrazeno 1 - 10
of 75 373
pro vyhledávání: '"Zakaria, A"'
Publikováno v:
xAI 2024 - The 2nd World Conference on eXplainable Artificial Intelligence, Jul 2024, La valette, Malta. pp.TBD
In the field of explainable AI, a vibrant effort is dedicated to the design of self-explainable models, as a more principled alternative to post-hoc methods that attempt to explain the decisions after a model opaquely makes them. However, this produc
Externí odkaz:
http://arxiv.org/abs/2409.16693
In this article we consider the surplus process of an insurance company within the CramerLundberg framework. We study the optimal reinsurance strategy and dividend distribution of an insurance company under proportional reinsurance, in which capital
Externí odkaz:
http://arxiv.org/abs/2409.12523
In this paper we explore the intersection of the Hassett divisor $\mathcal C_8$, parametrizing smooth cubic fourfolds $X$ containing a plane $P$ with other divisors $\mathcal C_i$. Notably we study the irreducible components of the intersections with
Externí odkaz:
http://arxiv.org/abs/2409.12032
Autor:
Devnani, Bhavika, Seto, Skyler, Aldeneh, Zakaria, Toso, Alessandro, Menyaylenko, Elena, Theobald, Barry-John, Sheaffer, Jonathan, Sarabia, Miguel
Humans can picture a sound scene given an imprecise natural language description. For example, it is easy to imagine an acoustic environment given a phrase like "the lion roar came from right behind me!". For a machine to have the same degree of comp
Externí odkaz:
http://arxiv.org/abs/2409.11369
Autor:
Aldeneh, Zakaria, Higuchi, Takuya, Jung, Jee-weon, Chen, Li-Wei, Shum, Stephen, Abdelaziz, Ahmed Hussen, Watanabe, Shinji, Likhomanenko, Tatiana, Theobald, Barry-John
Iterative self-training, or iterative pseudo-labeling (IPL)--using an improved model from the current iteration to provide pseudo-labels for the next iteration--has proven to be a powerful approach to enhance the quality of speaker representations. R
Externí odkaz:
http://arxiv.org/abs/2409.10791
Autor:
Chen, Li-Wei, Higuchi, Takuya, Bai, He, Abdelaziz, Ahmed Hussen, Rudnicky, Alexander, Watanabe, Shinji, Likhomanenko, Tatiana, Theobald, Barry-John, Aldeneh, Zakaria
Speech foundation models, such as HuBERT and its variants, are pre-trained on large amounts of unlabeled speech for various downstream tasks. These models use a masked prediction objective, where the model learns to predict information about masked i
Externí odkaz:
http://arxiv.org/abs/2409.10788
Autor:
Aldeneh, Zakaria, Thilak, Vimal, Higuchi, Takuya, Theobald, Barry-John, Likhomanenko, Tatiana
This study explores using embedding rank as an unsupervised evaluation metric for general-purpose speech encoders trained via self-supervised learning (SSL). Traditionally, assessing the performance of these encoders is resource-intensive and require
Externí odkaz:
http://arxiv.org/abs/2409.10787
Autor:
Pathi, Imdad Mahmud, Soo, John Y. H., Wee, Mao Jie, Zakaria, Sazatul Nadhilah, Ismail, Nur Azwin, Baugh, Carlton M., Manzoni, Giorgio, Gaztanaga, Enrique, Castander, Francisco J., Eriksen, Martin, Carretero, Jorge, Fernandez, Enrique, Garcia-Bellido, Juan, Miquel, Ramon, Padilla, Cristobal, Renard, Pablo, Sanchez, Eusebio, Sevilla-Noarbe, Ignacio, Tallada-Crespí, Pau
ANNZ is a fast and simple algorithm which utilises artificial neural networks (ANNs), it was known as one of the pioneers of machine learning approaches to photometric redshift estimation decades ago. We enhanced the algorithm by introducing new acti
Externí odkaz:
http://arxiv.org/abs/2409.09981
Autor:
Mahdaouy, Abdelkader El, Lamsiyah, Salima, Idrissi, Meryem Janati, Alami, Hamza, Yartaoui, Zakaria, Berrada, Ismail
Detecting and classifying suspicious or malicious domain names and URLs is fundamental task in cybersecurity. To leverage such indicators of compromise, cybersecurity vendors and practitioners often maintain and update blacklists of known malicious d
Externí odkaz:
http://arxiv.org/abs/2409.09143
Autor:
Patel, Zakaria, Wetzel, Sebastian J.
It has been demonstrated in many scientific fields that artificial neural networks like autoencoders or Siamese networks encode meaningful concepts in their latent spaces. However, there does not exist a comprehensive framework for retrieving this in
Externí odkaz:
http://arxiv.org/abs/2409.05305