1 citations · 1 across the 2 of their papers we have counts for
3 papers
Sequencing to Mitigate Catastrophic Forgetting in Continual Learning
Hesham G. Moussa, Aroosa Hameed, Arashmid Akhavain
To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, and exploit knowledge throughout its lifetime. This ability, known as Continual lear…
Distributed Learning and Inference Systems: A Networking Perspective
Hesham G. Moussa, Arashmid Akhavain, S. Maryam Hosseini +1
Machine learning models have achieved, and in some cases surpassed, human-level performance in various tasks, mainly through centralized training of static models and the use of la…
Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory
Omar Alhussein, Moshi Wei, Arashmid Akhavain
Split learning is a privacy-preserving distributed learning paradigm in which an ML model (e.g., a neural network) is split into two parts (i.e., an encoder and a decoder). The enc…