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pro vyhledávání: '"A Mert"'
We introduce a new erasure decoder that applies to arbitrary quantum LDPC codes. Dubbed the cluster decoder, it generalizes the decomposition idea of Vertical-Horizontal (VH) decoding introduced by Connelly et al. in 2022. Like the VH decoder, the id
Externí odkaz:
http://arxiv.org/abs/2412.08817
Over the past two decades, the Web Ontology Language (OWL) has been instrumental in advancing the development of ontologies and knowledge graphs, providing a structured framework that enhances the semantic integration of data. However, the reliabilit
Externí odkaz:
http://arxiv.org/abs/2412.08739
We introduce VOPy, an open-source Python library designed to address black-box vector optimization, where multiple objectives must be optimized simultaneously with respect to a partial order induced by a convex cone. VOPy extends beyond traditional m
Externí odkaz:
http://arxiv.org/abs/2412.06604
Assessing disease severity involving ordinal classes, where each class represents increasing levels of severity, benefit from loss functions that account for this ordinal structure. Traditional categorical loss functions, like Cross-Entropy (CE), oft
Externí odkaz:
http://arxiv.org/abs/2412.01246
Langevin algorithms are popular Markov Chain Monte Carlo methods for Bayesian learning, particularly when the aim is to sample from the posterior distribution of a parametric model, given the input data and the prior distribution over the model param
Externí odkaz:
http://arxiv.org/abs/2412.01993
Autor:
Chen, Panpan, Park, Seonyeong, Cam, Refik Mert, Huang, Hsuan-Kai, Oraevsky, Alexander A., Villa, Umberto, Anastasio, Mark A.
In certain three-dimensional (3D) applications of photoacoustic computed tomography (PACT), including \textit{in vivo} breast imaging, hemispherical measurement apertures that enclose the object within their convex hull are employed for data acquisit
Externí odkaz:
http://arxiv.org/abs/2412.01971
Link prediction is a fundamental problem in graph data. In its most realistic setting, the problem consists of predicting missing or future links between random pairs of nodes from the set of disconnected pairs. Graph Neural Networks (GNNs) have beco
Externí odkaz:
http://arxiv.org/abs/2412.00261
Volume parameterizations abound in recent literature, from the classic voxel grid to the implicit neural representation and everything in between. While implicit representations have shown impressive capacity and better memory efficiency compared to
Externí odkaz:
http://arxiv.org/abs/2411.13525
Recent advances in foundational Vision Language Models (VLMs) have reshaped the evaluation paradigm in computer vision tasks. These foundational models, especially CLIP, have accelerated research in open-vocabulary computer vision tasks, including Op
Externí odkaz:
http://arxiv.org/abs/2411.12044
Autor:
Peter, Benjamin M., Korkali, Mert
Reinforcement learning (RL) agents are powerful tools for managing power grids. They use large amounts of data to inform their actions and receive rewards or penalties as feedback to learn favorable responses for the system. Once trained, these agent
Externí odkaz:
http://arxiv.org/abs/2411.11180