1 code implementation • 15 May 2024 • Zichuan Liu, Tianchun Wang, Jimeng Shi, Xu Zheng, Zhuomin Chen, Lei Song, Wenqian Dong, Jayantha Obeysekera, Farhad Shirani, Dongsheng Luo
The design of the objective function builds upon the principle of information bottleneck (IB), and modifies the IB objective function to avoid trivial solutions and distributional shift issues.
no code implementations • 7 Feb 2024 • Xu Zheng, Farhad Shirani, Tianchun Wang, Shouwei Gao, Wenqian Dong, Wei Cheng, Dongsheng Luo
It is shown that the sample complexity of explanation-assisted learning can be arbitrarily smaller than explanation-agnostic learning.
no code implementations • 9 Dec 2023 • Rundong Huang, Farhad Shirani, Dongsheng Luo
Instead, we argue that a modified GIB principle may be used to avoid the aforementioned trivial solutions.
1 code implementation • 3 Oct 2023 • Xu Zheng, Farhad Shirani, Tianchun Wang, Wei Cheng, Zhuomin Chen, Haifeng Chen, Hua Wei, Dongsheng Luo
An explanation function for GNNs takes a pre-trained GNN along with a graph as input, to produce a `sufficient statistic' subgraph with respect to the graph label.
no code implementations • 9 Oct 2022 • Marian Temprana Alonso, Farhad Shirani, S. Sitharama Iyengar
Distributed estimation in the context of sensor networks is considered, where distributed agents are given a set of sensor measurements, and are tasked with estimating a target variable.
no code implementations • 8 Aug 2022 • Farhad Shirani, Hamidreza Aghasi
While reducing the number and resolution of the ADCs decreases power consumption, it also leads to a reduction in channel capacity due to the information loss induced by coarse quantization.