The turbulent jet ignition concept using prechambers is a promising solu...
Flamelet models are widely used in computational fluid dynamics to simul...
The information-theoretic framework promises to explain the predictive p...
Rate-distortion theory-based outlier detection builds upon the rationale...
We consider the problem of finding an input to a stochastic black box
fu...
Learning invariant representations that remain useful for a downstream t...
In this paper, we frame homogeneous-feature multi-task learning (MTL) as...
We survey information-theoretic approaches to the reduction of Markov ch...
Physics-informed neural networks (PINNs) seamlessly integrate data and
p...
This paper introduces a method for the detection of knock occurrences in...
We connect the problem of semi-supervised clustering to constrained Mark...
Recently a new type of deep learning method has emerged, called
physics-...
Drive towards improved performance of machine learning models has led to...
Complex systems, abstractly represented as networks, are ubiquitous in
e...
Distance-based classification is among the most competitive classificati...
We derive two sufficient conditions for a function of a Markov random fi...
We review the current literature concerned with information plane analys...
We propose a semi-supervised generative model, SeGMA, which learns a joi...
In this draft, which reports on work in progress, we 1) adapt the inform...
This short note presents results about the symmetric Jensen-Shannon
dive...
In this work, we characterize the outputs of individual neurons in a tra...
In this theory paper, we investigate training deep neural networks (DNNs...
We present an information-theoretic cost function for co-clustering, i.e...
The authors have recently defined the Rényi information dimension rate
d...
In this paper, we propose a semi-supervised clustering method, CEC-IB, t...