101 citations · 245 across the 16 of their papers we have counts for
6 papers · 1 filter
A Probabilistic Representation of DNNs: Bridging Mutual Information and Generalization
Xinjie Lan, Kenneth Barner
Recently, Mutual Information (MI) has attracted attention in bounding the generalization error of Deep Neural Networks (DNNs). However, it is intractable to accurately estimate the…
A Probabilistic Representation of Deep Learning for Improving The Information Theoretic Interpretability
Xinjie Lan, Kenneth E. Barner
In this paper, we propose a probabilistic representation of MultiLayer Perceptrons (MLPs) to improve the information-theoretic interpretability. Above all, we demonstrate that the…
PAC-Bayesian Generalization Bounds for MultiLayer Perceptrons
Xinjie Lan, Xin Guo, Kenneth E. Barner
We study PAC-Bayesian generalization bounds for Multilayer Perceptrons (MLPs) with the cross entropy loss. Above all, we introduce probabilistic explanations for MLPs in two aspect…
Explicitly Bayesian Regularizations in Deep Learning
Xinjie Lan, Kenneth E. Barner
Generalization is essential for deep learning. In contrast to previous works claiming that Deep Neural Networks (DNNs) have an implicit regularization implemented by the stochastic…
A Probabilistic Representation of Deep Learning
Xinjie Lan, Kenneth E. Barner
In this work, we introduce a novel probabilistic representation of deep learning, which provides an explicit explanation for the Deep Neural Networks (DNNs) in three aspects: (i) n…
Exploiting Restricted Boltzmann Machines and Deep Belief Networks in Compressed Sensing
Luisa F. Polania, Kenneth E. Barner
This paper proposes a CS scheme that exploits the representational power of restricted Boltzmann machines and deep learning architectures to model the prior distribution of the spa…