Zobrazeno 1 - 10
of 3 785
pro vyhledávání: '"Schuman P"'
Autor:
Nüßlein, Jonas, Schuman, Daniëlle, Bucher, David, Mohseni, Naeimeh, Ghosh, Kumar, O'Meara, Corey, Cortiana, Giorgio, Linnhoff-Popien, Claudia
The transition to 100% renewable energy requires new techniques for managing energy networks, such as dividing them into sensible subsets of prosumers called micro-grids. Doing so in an optimal manner is a difficult optimization problem, as it can be
Externí odkaz:
http://arxiv.org/abs/2408.04366
Autor:
Kölle, Michael, Ahouzi, Afrae, Debus, Pascal, Çetiner, Elif, Müller, Robert, Schuman, Daniëlle, Linnhoff-Popien, Claudia
Quantum one-class support vector machines leverage the advantage of quantum kernel methods for semi-supervised anomaly detection. However, their quadratic time complexity with respect to data size poses challenges when dealing with large datasets. In
Externí odkaz:
http://arxiv.org/abs/2407.20753
Autor:
Rohe, Tobias, Schuman, Daniëlle, Nüßlein, Jonas, Sünkel, Leo, Stein, Jonas, Linnhoff-Popien, Claudia
The performance of the Variational Quantum Eigensolver (VQE) is promising compared to other quantum algorithms, but also depends significantly on the appropriate design of the underlying quantum circuit. Recent research by Bowles, Ahmend \& Schuld, 2
Externí odkaz:
http://arxiv.org/abs/2407.17204
Autor:
Tan, Ou, Greenfield, David S., Francis, Brian A., Varma, Rohit, Schuman, Joel S., Huang, David, Choi, Dongseok
Precis: A hybrid deep-learning model combines NFL reflectance and other OCT parameters to improve glaucoma diagnosis. Objective: To investigate if a deep learning model could be used to combine nerve fiber layer (NFL) reflectance and other OCT parame
Externí odkaz:
http://arxiv.org/abs/2406.03663
Autor:
Tan, Ou, Choi, Dongseok, Chen, Aiyin, Greenfield, David S., Francis, Brian A., Varma, Rohit, Schuman, Joel S., Huang, David, Group, Advanced Imaging for Glaucoma Study
Purpose: To evaluate nerve fiber layer (NFL) reflectance for glaucoma diagnosis using a large dataset. Methods: Participants were imaged with 4.9mm ONH scans using spectral-domain optical coherence tomography (OCT). The NFL reflectance map was recons
Externí odkaz:
http://arxiv.org/abs/2406.00170
Autor:
Gobin, Derek, Snyder, Shay, Cong, Guojing, Kulkarni, Shruti R., Schuman, Catherine, Parsa, Maryam
Many of today's most interesting questions involve understanding and interpreting complex relationships within graph-based structures. For instance, in materials science, predicting material properties often relies on analyzing the intricate network
Externí odkaz:
http://arxiv.org/abs/2405.04478
Autor:
Snyder, Shay, Clerico, Victoria, Cong, Guojing, Kulkarni, Shruti, Schuman, Catherine, Risbud, Sumedh R., Parsa, Maryam
Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural networks to extract features within
Externí odkaz:
http://arxiv.org/abs/2404.17048
Autor:
Mansky, Maximilian Balthasar, Nüßlein, Jonas, Bucher, David, Schuman, Daniëlle, Zielinski, Sebastian, Linnhoff-Popien, Claudia
Publikováno v:
2023 IEEE International Conference on Quantum Computing and Engineering (QCE)
Due to the advances in the manufacturing of quantum hardware in the recent years, significant research efforts have been directed towards employing quantum methods to solving problems in various areas of interest. Thus a plethora of novel quantum met
Externí odkaz:
http://arxiv.org/abs/2402.16341
Spiking neural networks are powerful computational elements that pair well with event-based cameras (EBCs). In this work, we present two spiking neural network architectures that process events from EBCs: one that isolates and filters out events base
Externí odkaz:
http://arxiv.org/abs/2401.15212
Autor:
Kölle, Michael, Ahouzi, Afrae, Debus, Pascal, Müller, Robert, Schuman, Danielle, Linnhoff-Popien, Claudia
Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision. Utilizing the one-class Support Vector Machine alongside a quantum kernel, known for its class
Externí odkaz:
http://arxiv.org/abs/2312.09174