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08/03/2023
Finding the Optimum Design of Large Gas Engines Prechambers Using CFD and Bayesian Optimization
The turbulent jet ignition concept using prechambers is a promising solu...
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08/03/2023
Bringing Chemistry to Scale: Loss Weight Adjustment for Multivariate Regression in Deep Learning of Thermochemical Processes
Flamelet models are widely used in computational fluid dynamics to simul...
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03/01/2023
Information Plane Analysis for Dropout Neural Networks
The information-theoretic framework promises to explain the predictive p...
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02/22/2023
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory
Rate-distortion theory-based outlier detection builds upon the rationale...
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01/11/2023
Robust Bayesian Target Value Optimization
We consider the problem of finding an input to a stochastic black box fu...
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11/02/2022
FUNCK: Information Funnels and Bottlenecks for Invariant Representation Learning
Learning invariant representations that remain useful for a downstream t...
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05/31/2022
Compressed Hierarchical Representations for Multi-Task Learning and Task Clustering
In this paper, we frame homogeneous-feature multi-task learning (MTL) as...
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04/29/2022
Information-Theoretic Reduction of Markov Chains
We survey information-theoretic approaches to the reduction of Markov ch...
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03/25/2022
Understanding the Difficulty of Training Physics-Informed Neural Networks on Dynamical Systems
Physics-informed neural networks (PINNs) seamlessly integrate data and p...
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01/18/2022
Knock Detection in Combustion Engine Time Series Using a Theory-Guided 1D Convolutional Neural Network Approach
This paper introduces a method for the detection of knock occurrences in...
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12/17/2021
Semi-Supervised Clustering via Markov Chain Aggregation
We connect the problem of semi-supervised clustering to constrained Mark...
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05/03/2021
On the Pareto Front of Physics-Informed Neural Networks
Recently a new type of deep learning method has emerged, called physics-...
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01/30/2021
Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures
Drive towards improved performance of machine learning models has led to...
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01/21/2021
Synwalk – Community Detection via Random Walk Modelling
Complex systems, abstractly represented as networks, are ubiquitous in e...
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08/18/2020
A Formally Robust Time Series Distance Metric
Distance-based classification is among the most competitive classificati...
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05/28/2020
On Functions of Markov Random Fields
We derive two sufficient conditions for a function of a Markov random fi...
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03/21/2020
On Information Plane Analyses of Neural Network Classifiers – A Review
We review the current literature concerned with information plane analys...
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06/21/2019
SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder
We propose a semi-supervised generative model, SeGMA, which learns a joi...
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06/06/2019
Class-Conditional Compression and Disentanglement: Bridging the Gap between Neural Networks and Naive Bayes Classifiers
In this draft, which reports on work in progress, we 1) adapt the inform...
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12/05/2018
A Short Note on the Jensen-Shannon Divergence between Simple Mixture Distributions
This short note presents results about the symmetric Jensen-Shannon dive...
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04/18/2018
Understanding Individual Neuron Importance Using Information Theory
In this work, we characterize the outputs of individual neurons in a tra...
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02/27/2018
How (Not) To Train Your Neural Network Using the Information Bottleneck Principle
In this theory paper, we investigate training deep neural networks (DNNs...
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01/02/2018
Co-Clustering via Information-Theoretic Markov Aggregation
We present an information-theoretic cost function for co-clustering, i.e...
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12/21/2017
On the Information Dimension of Multivariate Gaussian Processes
The authors have recently defined the Rényi information dimension rate d...
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05/03/2017
