59 citations · 95 across the 15 of their papers we have counts for
16 papers
On Functions of Markov Random Fields
Bernhard C. Geiger, Ali Al-Bashabsheh
We derive two sufficient conditions for a function of a Markov random field (MRF) on a given graph to be a MRF on the same graph. The first condition is information-theoretic and p…
Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers
Masoumeh Soflaei, Hongyu Guo, Ali Al-Bashabsheh +2
We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation lea…
Neural Entropic Estimation: A faster path to mutual information estimation
Chung Chan, Ali Al-Bashabsheh, Hing Pang Huang +3
We point out a limitation of the mutual information neural estimation (MINE) where the network fails to learn at the initial training phase, leading to slow convergence in the numb…
Info-Clustering: An Efficient Algorithm by Network Information Flow
Chung Chan, Ali Al-Bashabsheh, Qiaoqiao Zhou
Motivated by the fact that entities in a social network or biological system often interact by exchanging information, we propose an efficient info-clustering algorithm that can gr…
Duality between Feature Selection and Data Clustering
Chung Chan, Ali Al-Bashabsheh, Qiaoqiao Zhou +1
The feature-selection problem is formulated from an information-theoretic perspective. We show that the problem can be efficiently solved by an extension of the recently proposed i…
On Information-Theoretic Characterizations of Markov Random Fields and Subfields
Raymond W. Yeung, Ali Al-Bashabsheh, Chao Chen +2
Let form a Markov random field (MRF) represented by an undirected graph , and be a subset of . We determine the smallest graph that can always rep…