research
∙
03/14/2023
Self-attention for Enhanced OAMP Detection in MIMO Systems
Multiple-Input Multiple-Output (MIMO) systems are essential for wireless...
share
research
∙
06/04/2022
Active Bayesian Causal Inference
Causal discovery and causal reasoning are classically treated as separat...
research
∙
02/10/2022
Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities
Radio Frequency (RF) breakdowns are one of the most prevalent limiting f...
research
∙
01/25/2022
Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation
Radar sensors are crucial for environment perception of driver assistanc...
research
∙
10/05/2021
Distribution Mismatch Correction for Improved Robustness in Deep Neural Networks
Deep neural networks rely heavily on normalization methods to improve th...
research
∙
08/04/2021
Lung Sound Classification Using Co-tuning and Stochastic Normalization
In this paper, we use pre-trained ResNet models as backbone architecture...
research
∙
04/30/2021
Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolitional Neural Networks
Large annotated lung sound databases are publicly available and might be...
research
∙
04/29/2021
Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation
Autonomous driving highly depends on capable sensors to perceive the env...
research
∙
04/14/2021
End-to-end Keyword Spotting using Neural Architecture Search and Quantization
This paper introduces neural architecture search (NAS) for the automatic...
research
∙
03/24/2021
Blind Speech Separation and Dereverberation using Neural Beamforming
In this paper, we present the Blind Speech Separation and Dereverberatio...
research
∙
12/18/2020
Resource-efficient DNNs for Keyword Spotting using Neural Architecture Search and Quantization
This paper introduces neural architecture search (NAS) for the automatic...
research
∙
12/04/2020
Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals
Radar sensors are crucial for environment perception of driver assistanc...
research
∙
11/25/2020
Quantized Neural Networks for Radar Interference Mitigation
Radar sensors are crucial for environment perception of driver assistanc...
research
∙
10/22/2020
On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks
We present two methods to reduce the complexity of Bayesian network (BN)...
research
∙
08/21/2020
Differentiable TAN Structure Learning for Bayesian Network Classifiers
Learning the structure of Bayesian networks is a difficult combinatorial...
research
∙
07/22/2020
Resource-Efficient Speech Mask Estimation for Multi-Channel Speech Enhancement
While machine learning techniques are traditionally resource intensive, ...
research
∙
07/22/2020
Wasserstein Routed Capsule Networks
Capsule networks offer interesting properties and provide an alternative...
research
∙
01/07/2020
Resource-Efficient Neural Networks for Embedded Systems
While machine learning is traditionally a resource intensive task, embed...
research
∙
10/10/2019
Deep Structured Mixtures of Gaussian Processes
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression...
research
∙
07/10/2019
Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version)
Models play an essential role in the design process of cyber-physical sy...
research
∙
06/24/2019
Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural Networks
Driver assistance systems as well as autonomous cars have to rely on sen...
research
∙
06/12/2019
Parameterized Structured Pruning for Deep Neural Networks
As a result of the growing size of Deep Neural Networks (DNNs), the gap ...
research
∙
05/26/2019
Bayesian Learning of Sum-Product Networks
Sum-product networks (SPNs) are flexible density estimators and have rec...
research
∙
05/20/2019
Optimisation of Overparametrized Sum-Product Networks
It seems to be a pearl of conventional wisdom that parameter learning in...
research
∙
12/05/2018
Efficient and Robust Machine Learning for Real-World Systems
While machine learning is traditionally a resource intensive task, embed...
research
∙
12/04/2018
Self-Guided Belief Propagation -- A Homotopy Continuation Method
We propose self-guided belief propagation (SBP) that modifies belief pro...
research
∙
10/16/2018
Sound event detection using weakly-labeled semi-supervised data with GCRNNS, VAT and Self-Adaptive Label Refinement
In this paper, we present a gated convolutional recurrent neural network...
research
∙
09/12/2018
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
While Gaussian processes (GPs) are the method of choice for regression t...
research
∙
07/06/2018
Sum-Product Networks for Sequence Labeling
We consider higher-order linear-chain conditional random fields (HO-LC-C...
research
∙
06/04/2018
Automatic Clustering of a Network Protocol with Weakly-Supervised Clustering
Abstraction is a fundamental part when learning behavioral models of sys...
research
∙
10/10/2017
Safe Semi-Supervised Learning of Sum-Product Networks
In several domains obtaining class annotations is expensive while at the...
research
∙
05/20/2016
Fixed Points of Belief Propagation -- An Analysis via Polynomial Homotopy Continuation
Belief propagation (BP) is an iterative method to perform approximate in...
research
∙
01/22/2016
On the Latent Variable Interpretation in Sum-Product Networks
One of the central themes in Sum-Product networks (SPNs) is the interpre...
research
∙
06/27/2012
