Zobrazeno 1 - 10
of 280
pro vyhledávání: '"Esfahanizadeh A"'
Large language models have drastically changed the prospects of AI by introducing technologies for more complex natural language processing. However, current methodologies to train such LLMs require extensive resources including but not limited to la
Externí odkaz:
http://arxiv.org/abs/2410.21548
Semantic communication, when examined through the lens of joint source-channel coding (JSCC), maps source messages directly into channel input symbols, where the measure of success is defined by end-to-end distortion rather than traditional metrics s
Externí odkaz:
http://arxiv.org/abs/2407.05487
Autor:
Esfahanizadeh, Homa, Vasudevan, Vipindev Adat, Kim, Benjamin D., Siva, Shruti, Kim, Jennifer, Cohen, Alejandro, Médard, Muriel
Network slicing has emerged as an integral concept in 5G, aiming to partition the physical network infrastructure into isolated slices, customized for specific applications. We theoretically formulate the key performance metrics of an application, in
Externí odkaz:
http://arxiv.org/abs/2404.17686
Modern computationally-intensive applications often operate under time constraints, necessitating acceleration methods and distribution of computational workloads across multiple entities. However, the outcome is either achieved within the desired ti
Externí odkaz:
http://arxiv.org/abs/2402.07229
Autor:
Kale, Kaan, Esfahanizadeh, Homa, Elias, Noel, Baser, Oguzhan, Medard, Muriel, Vishwanath, Sriram
With the exponential growth in data volume and the emergence of data-intensive applications, particularly in the field of machine learning, concerns related to resource utilization, privacy, and fairness have become paramount. This paper focuses on t
Externí odkaz:
http://arxiv.org/abs/2402.05132
The problem of mismatched guesswork considers the additional cost incurred by using a guessing function which is optimal for a distribution $q$ when the random variable to be guessed is actually distributed according to a different distribution $p$.
Externí odkaz:
http://arxiv.org/abs/2305.03850
Autor:
Narges Mazloomi, Ebrahim Salehifar, Mohammadhosein Esfahanizadeh, Hashem Ghezelsofla, Keyvan Mahdavi Mashaki, Esmaeil Babanezhad, Laleh Karimzadeh
Publikováno v:
Journal of Mazandaran University of Medical Sciences, Vol 34, Iss 236, Pp 125-130 (2024)
Background and purpose: Today, ensuring food security for the inhabitants of the earth, and preserving agricultural production from destruction due to drought, pests, and diseases, seems more necessary than ever. Farmers across the world are forced t
Externí odkaz:
https://doaj.org/article/09a3bdfd92aa4550aff2ba759b11ec3b
Autor:
Laleh Karimzadeh, Ebrahim Salehifar, Mohammadhosein Esfahanizadeh, Hashem Ghezelsofla, Keyvan Mahdavi Mashaki, Esmaeil Babanezhad, Narges Mazloomi
Publikováno v:
Journal of Mazandaran University of Medical Sciences, Vol 34, Iss 236, Pp 120-124 (2024)
Background and purpose: The employment of pesticides for the prevention, elimination, and reduction of pests detrimental to agricultural products throughout various stages of crop cultivation is essential for improving the health of these products; n
Externí odkaz:
https://doaj.org/article/4c3674c0d50547c19610d79a8a386777
Autor:
Esfahanizadeh, Homa, Yala, Adam, D'Oliveira, Rafael G. L., Jaba, Andrea J. D., Quach, Victor, Duffy, Ken R., Jaakkola, Tommi S., Vaikuntanathan, Vinod, Ghobadi, Manya, Barzilay, Regina, Médard, Muriel
Allowing organizations to share their data for training of machine learning (ML) models without unintended information leakage is an open problem in practice. A promising technique for this still-open problem is to train models on the encoded data. O
Externí odkaz:
http://arxiv.org/abs/2304.00047
The use of mutual information as a tool in private data sharing has remained an open challenge due to the difficulty of its estimation in practice. In this paper, we propose InfoShape, a task-based encoder that aims to remove unnecessary sensitive in
Externí odkaz:
http://arxiv.org/abs/2210.15034