Search Results for author:
Found 21 papers, 4 papers with code
Computing Marginal and Conditional Divergences between Decomposable Models with Applications
Doing so tractably is non-trivial as we need to decompose the divergence between these distributions and therefore, require a decomposition over the marginal and conditional distributions of these models.
Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning
The resulting attributions can be interpreted as explanations for why a specific circuit works well for a given task, improving the understanding of how to construct parameterized (or variational) quantum circuits, and fostering their human interpretability in general.
Explainable Artificial Intelligence (XAI)
Quantum Machine Learning
Shapley Values with Uncertain Value Functions
We propose a novel definition of Shapley values with uncertain value functions based on first principles using probability theory.
Full Kullback-Leibler-Divergence Loss for Hyperparameter-free Label Distribution Learning
The concept of Label Distribution Learning (LDL) is a technique to stabilize classification and regression problems with ambiguous and/or imbalanced labels.
On Quantum Circuits for Discrete Graphical Models
In this work, we provide the first method that allows one to provably generate unbiased and independent samples from general discrete factor models with a quantum circuit.
Informed Pre-Training on Prior Knowledge
In this paper, we propose a novel informed machine learning approach and suggest to pre-train on prior knowledge.
Towards Bundle Adjustment for Satellite Imaging via Quantum Machine Learning
Given is a set of images, where all images show views of the same area at different points in time and from different viewpoints.
QUBOs for Sorting Lists and Building Trees
We show that the fundamental tasks of sorting lists and building search trees or heaps can be modeled as quadratic unconstrained binary optimization problems (QUBOs).
Computing Divergences between Discrete Decomposable Models
We show that we are able to compute a wide family of functionals and divergences, such as the alpha-beta divergence, between two decomposable models, i. e. chordal Markov networks, in time exponential to the treewidth of these models.
On the effects of biased quantum random numbers on the initialization of artificial neural networks
Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy intermediate-scale quantum (NISQ) devices.
The Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learning
On the other hand, care labels are the result of a certification suite that tests whether stated guarantees hold.
Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework
How can they be guaranteed for a certain implementation of a machine learning model?
Street-Map Based Validation of Semantic Segmentation in Autonomous Driving
Lastly, we present quantitative results on the Cityscapes dataset indicating that our validation approach can indeed uncover errors in semantic segmentation masks.
Quantum Circuit Evolution on NISQ Devices
The results provide intuition on how randomized search heuristics behave on actual quantum hardware and lay out a path for further refinement of evolutionary quantum gate circuits.
Resource-Constrained On-Device Learning by Dynamic Averaging
This paper investigates an approach to communication-efficient on-device learning of integer exponential families that can be executed on low-power processors, is privacy-preserving, and effectively minimizes communication.
LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations
Exploiting big data knowledge on small devices will pave the way for building truly cognitive Internet of Things (IoT) systems.
The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization
The Boolean product is a disjunction of rank-1 binary matrices, each describing a feature-relation, called pattern, for a group of samples.
The PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization
In contrast to existing work, the new algorithm minimizes the description length of the resulting factorization.
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
Machine Learning Based Uplink Transmission Power Prediction for LTE and Upcoming 5G Networks using Passive Downlink Indicators
Energy-aware system design is an important optimization task for static and mobile Internet of Things (IoT)-based sensor nodes, especially for highly resource-constrained vehicles such as mobile robotic systems.
Networking and Internet Architecture
