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3d face reconstruction using deep learning.
Student thesis : Master
Achieving Long Term Fairness through Curiosity Driven Reinforcement Learning: How intrinsic motivation influences fairness in algorithmic decision making
A comprehensive overview of ood detection methods.
Student thesis : Bachelor
Activity Recognition Using Deep Learning in Videos under Clinical Setting
A data cleaning assistant, a data cleaning assistant for machine learning, a deep learning approach for clustering a multi-class dataset, aerial imagery pixel-level segmentation, a framework for understanding business process remaining time predictions, a hybrid model for pedestrian motion prediction, algorithms for center-based trajectory clustering, allocation decision-making in service supply chain with deep reinforcement learning, analyzing multi-periodic time series data using the generalized langevin equation, analyzing policy gradient approaches towards rapid policy transfer, an empirical study on dynamic curriculum learning in information retrieval, an explainable approach to multi-contextual fake news detection, an exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks, anomaly detection in image data sets using disentangled representations, anomaly detection in polysomnography signals using ai, anomaly detection in text data using deep generative models, anomaly detection on dynamic graph, anomaly detection on finite multivariate time series from semi-automated screwing applications, anomaly detection on multivariate time series using gans, anomaly detection on vibration data, application of p&id symbol detection and classification for generation of material take-off documents (mtos), applications of deep generative models to tokamak nuclear fusion, a similarity based meta-learning approach to building pipeline portfolios for automated machine learning, aspect-based few-shot learning, aspect-based few-shot learning, assessing bias and fairness in machine learning through a causal lens, assessing fairness in anomaly detection: a framework for developing a context-aware fairness tool to assess rule-based models, a study of an open-ended strategy for learning complex locomotion skills, a systematic determination of metrics for classification tasks in openml, a universally applicable emm framework, automated estimation of stochastic lattice model parameters: modeling tumor evolution: simulating metastasis with the cellular potts model, automated machine learning with gradient boosting and meta-learning, automated object recognition of solar panels in aerial photographs: a case study in the liander service area, automatic data cleaning, automatic data cleaning tool for openml, automatic scoring of short open-ended questions, automatic synthesis of machine learning pipelines consisting of pre-trained models for multimodal data, automating feature generation and selection in a transparent, scalable manner, automating string encoding in automl, autoregressive neural networks to model electroencephalograpy signals, balancing efficiency and fairness on ride-hailing platforms via reinforcement learning, benchmarking audio deepfake detection, better clustering evaluation for the openml evaluation engine, bi-level pipeline optimization for scalable automl, block-sparse evolutionary training using weight momentum evolution: training methods for hardware efficient sparse neural networks.
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