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Found 15 papers, 5 papers with code
The Dynamics of a Bicycle on a Pump Track -- First Results on Modeling and Optimal Control
We investigate the dynamics of a bicycle on an uneven mountain bike track split into straight sections with small jumps (kickers) and banked corners.
Distilling Influences to Mitigate Prediction Churn in Graph Neural Networks
Our work explores this phenomenon in graph neural networks by investigating differences between models differing only in their initializations in their utilized features for predictions.
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks
Our study reveals new theoretical insights into over-smoothing and feature over-correlation in deep graph neural networks.
Curvature-based Pooling within Graph Neural Networks
CurvPool exploits the notion of curvature of a graph to adaptively identify structures responsible for both over-smoothing and over-squashing.
Distributed LSTM-Learning from Differentially Private Label Proportions
Data privacy and decentralised data collection has become more and more popular in recent years.
LOSDD: Leave-Out Support Vector Data Description for Outlier Detection
Support Vector Machines have been successfully used for one-class classification (OCSVM, SVDD) when trained on clean data, but they work much worse on dirty data: outliers present in the training data tend to become support vectors, and are hence considered "normal".
Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks
Our framework can be combined with any spatio-temporal Graph Neural Network, that exploits spatio-temporal correlations with surrounding observed locations by using the network's graph structure.
Certified Data Removal in Sum-Product Networks
Data protection regulations like the GDPR or the California Consumer Privacy Act give users more control over the data that is collected about them.
Evaluating Machine Unlearning via Epistemic Uncertainty
There has been a growing interest in Machine Unlearning recently, primarily due to legal requirements such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act.
Transforming PageRank into an Infinite-Depth Graph Neural Network
Popular graph neural networks are shallow models, despite the success of very deep architectures in other application domains of deep learning.
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
In contrast, deep probabilistic models such as sum-product networks (SPNs) capture joint distributions in a tractable fashion, but still lack the expressive power of intractable models based on deep neural networks.
Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction
The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast.
Networking and Internet Architecture
System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic
While the development of fully autonomous vehicles is one of the major research fields in the Intelligent Transportation Systems (ITSs) domain, the upcoming longterm transition period - the hybrid vehicular traffic - is often neglected.
Networking and Internet Architecture
Machine learning based context-predictive car-to-cloud communication using multi-layer connectivity maps for upcoming 5G networks
While cars were only considered as means of personal transportation for a long time, they are currently transcending to mobile sensor nodes that gather highly up-to-date information for crowdsensing-enabled big data services in a smart city context.
Networking and Internet Architecture
Efficient Machine-type Communication using Multi-metric Context-awareness for Cars used as Mobile Sensors in Upcoming 5G Networks
Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of static and mobile Internet of Things (IoT) systems.
Networking and Internet Architecture
