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- Center for Integrated Quantum Science and TechnologyDE70 papers
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16 papers · 1 filter
Representing and Detecting Label Ambiguity in IMU-Based Exercise Evaluation
Andreas Spilz, Heiko Oppel, Michael Munz
Home-based physiotherapy is performed without supervision, which leads to incorrect execution and motivates systems that assess movement automatically from inertial measurement uni…
Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting
Abhinav Das, Stephan Schlüter
Accurate electricity price forecasting is critical for strategic decision-making in deregulated electricity markets, where volatility stems from complex supply-demand dynamics and…
Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting
Andreas Lebedev, Abhinav Das, Sven Pappert +1
Precise probabilistic forecasts are fundamental for energy risk management, and there is a wide range of both statistical and machine learning models for this purpose. Inherent to…
Assessing Trustworthiness of AI Training Dataset using Subjective Logic -- A Use Case on Bias
Koffi Ismael Ouattara, Ioannis Krontiris, Theo Dimitrakos +1
As AI systems increasingly rely on training data, assessing dataset trustworthiness has become critical, particularly for properties like fairness or bias that emerge at the datase…
Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs
Heiko Oppel, Andreas Spilz, Michael Munz
Denoising diffusion probabilistic models are able to generate synthetic sensor signals. The training process of such a model is controlled by a loss function which measures the dif…
A Transformer-based Autoregressive Decoder Architecture for Hierarchical Text Classification
Younes Yousef, Lukas Galke, Ansgar Scherp
Recent approaches in hierarchical text classification (HTC) rely on the capabilities of a pre-trained transformer model and exploit the label semantics and a graph encoder for the…