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dustintran.com | ||
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tiao.io
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| | | | | This thesis explores the intersection of deep learning and probabilistic machine learning to enhance the capabilities of artificial intelligence. It addresses the limitations of Gaussian processes (GPs) in practical applications, particularly in comparison to neural networks (NNs), and proposes advancements such as improved approximations and a novel formulation of Bayesian optimization (BO) that seamlessly integrates deep learning methods. The contributions aim to enrich the interplay between deep learn... | |
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tiao.io
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| | | | | This post demonstrates how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions, using density ratio estimation by probabilistic classification. | |
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tiao.io
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| | | | | We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly. To address the problem of propagating gradients through latent variables draw... | |
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www.v7labs.com
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| | | Learn about the different types of neural network architectures. | ||
