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Publications
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale
We propose a new unsupervised anomaly detection method based on the sliced-Wasserstein distance for training data selection in machine learn… (see more)ing approaches. Our filtering technique is interesting for decision-making pipelines deploying machine learning models in critical sectors, e.g., power systems, as it offers a conservative data selection and an optimal transport interpretation. To ensure the scalability of our method, we provide two efficient approximations. The first approximation processes reduced-cardinality representations of the datasets concurrently. The second makes use of a computationally light Euclidian distance approximation. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We present the filtering patterns of our method on synthetic datasets and numerically benchmark our method for training data selection. Finally, we employ our method as part of a first forecasting benchmark for our open-source dataset.
Trade‐off of different deep learning‐based auto‐segmentation approaches for treatment planning of pediatric craniospinal irradiation autocontouring of OARs for pediatric CSI
As auto‐segmentation tools become integral to radiotherapy, more commercial products emerge. However, they may not always suit our needs. … (see more)One notable example is the use of adult‐trained commercial software for the contouring of organs at risk (OARs) of pediatric patients.
