Understanding Deep Generative Models in Probabilistic Machine Learning
This content explores various deep generative models such as Variational Autoencoders and Generative Adversarial Networks used in Probabilistic Machine Learning. It discusses the construction of generative models using neural networks and Gaussian processes, with a focus on techniques like VAEs and
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Improving Qubit Readout with Autoencoders in Quantum Science Workshop
Dispersive qubit readout, standard models, and the use of autoencoders for improving qubit readout in quantum science are discussed in the workshop led by Piero Luchi. The workshop covers topics such as qubit-cavity systems, dispersive regime equations, and the classification of qubit states through
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Machine Learning and Generative Models in Particle Physics Experiments
Explore the utilization of machine learning algorithms and generative models for accurate simulation in particle physics experiments. Understand the concepts of supervised, unsupervised, and semi-supervised learning, along with generative models like Variational Autoencoder and Gaussian Mixtures. Le
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Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling
This study explores the use of Riemannian Normalizing Flow on Variational Wasserstein Autoencoder (WAE) to address the KL vanishing problem in Variational Autoencoders (VAE) for text modeling. By leveraging Riemannian geometry, the Normalizing Flow approach aims to prevent the collapse of the poster
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