Apr 27, 2022 · We propose a new concept called Weight Separation of deep neural networks (DNNs), which enables memory-efficient and accurate deep multitask learning.
Abstract—We propose a new concept called Weight Separation of deep neural networks (DNNs), which enables memory-efficient and accurate deep multitask ...
We propose a new concept called Weight Separation of deep neural networks (DNNs), which enables memory-efficient and accurate deep multitask learning.
It enables extreme low-memory and accurate deep multitask learning with two types of weight parameters applied to two levels of the system memory hierarchy, ...
Duration: 26:18
Posted: Apr 1, 2022
Posted: Apr 1, 2022
Weight-sharing techniques enhance the feasibility of deploying Deep Neural Networks (DNNs) on low-memory devices, applicable in both single-task [1], [84], [85] ...
] "Weight Separation for Memory-Efficient and Accurate Deep Multitask Learning". The 20th International Conference on Pervasive Computing and Communications ...
2023. Weight Separation for Memory-Efficient and Accurate Deep Multitask Learning. S Lee, S Nirjon. 2022 IEEE International Conference on Pervasive Computing ...
Sep 9, 2024 · Weight Separation for Memory-Efficient and Accurate Deep Multitask Learning. Conference Paper. Mar 2022. Seulki Lee · Shahriar Nirjon · View.
Jun 15, 2020 · This paper introduces the concept of Neural Weight Virtualization - which enables fast and scalable in-memory multitask deep learning on memory-constrained ...