Zobrazeno 1 - 10
of 5 340
pro vyhledávání: '"Nazemi A"'
Autor:
Nazemi, Niousha, Tavallaie, Omid, Chen, Shuaijun, Mandalari, Anna Maria, Thilakarathna, Kanchana, Holz, Ralph, Haddadi, Hamed, Zomaya, Albert Y.
Federated Learning (FL) is a promising distributed learning framework designed for privacy-aware applications. FL trains models on client devices without sharing the client's data and generates a global model on a server by aggregating model updates.
Externí odkaz:
http://arxiv.org/abs/2409.01722
Current strategies for solving image-based inverse problems apply latent diffusion models to perform posterior sampling.However, almost all approaches make no explicit attempt to explore the solution space, instead drawing only a single sample from a
Externí odkaz:
http://arxiv.org/abs/2408.13868
Autor:
Nazemi, Niousha, Tavallaie, Omid, Mandalari, Anna Maria, Haddadi, Hamed, Holz, Ralph, Zomaya, Albert Y.
This paper investigates the impact of internet centralization on DNS provisioning, particularly its effects on vulnerable populations such as the indigenous people of Australia. We analyze the DNS dependencies of Australian government domains that se
Externí odkaz:
http://arxiv.org/abs/2408.12958
The pedestrian stress level is shown to significantly influence human cognitive processes and, subsequently, decision-making, e.g., the decision to select a gap and cross a street. This paper systematically studies the stress experienced by a pedestr
Externí odkaz:
http://arxiv.org/abs/2408.11769
Federated Learning (FL) is a promising privacy-aware distributed learning framework that can be deployed on various devices, such as mobile phones, desktops, and devices equipped with CPUs or GPUs. In the context of server-based Federated Learning as
Externí odkaz:
http://arxiv.org/abs/2408.08699
Autor:
Jamali, Ali, Nazemi, Azadeh, Sami, Ashkan, Bahrololoom, Rosemina, Paydar, Shahram, Shakibafar, Alireza
Trauma significantly impacts global health, accounting for over 5 million deaths annually, which is comparable to mortality rates from diseases such as tuberculosis, AIDS, and malaria. In Iran, the financial repercussions of road traffic accidents re
Externí odkaz:
http://arxiv.org/abs/2408.02012
Federated Learning (FL) is a decentralized machine learning approach where client devices train models locally and send them to a server that performs aggregation to generate a global model. FL is vulnerable to model inversion attacks, where the serv
Externí odkaz:
http://arxiv.org/abs/2405.01144
As Vision Transformers (ViTs) increasingly set new benchmarks in computer vision, their practical deployment on inference engines is often hindered by their significant memory bandwidth and (on-chip) memory footprint requirements. This paper addresse
Externí odkaz:
http://arxiv.org/abs/2402.06004
This paper presents a mixed-computation neural network processing approach for edge applications that incorporates low-precision (low-width) Posit and low-precision fixed point (FixP) number systems. This mixed-computation approach employs 4-bit Posi
Externí odkaz:
http://arxiv.org/abs/2312.02210
Autor:
Spicker, Dylan, Nazemi, Amir, Hutchinson, Joy, Fieguth, Paul, Kirkpatrick, Sharon I., Wallace, Michael, Dodd, Kevin W.
Dietary intake data are routinely drawn upon to explore diet-health relationships. However, these data are often subject to measurement error, distorting the true relationships. Beyond measurement error, there are likely complex synergistic and somet
Externí odkaz:
http://arxiv.org/abs/2311.09338