
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers
Bayesian neural networks provide a direct and natural way to extend stan...
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Bayesian Neural Networks: Essentials
Bayesian neural networks utilize probabilistic layers that capture uncer...
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Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models
Probabilistic deep learning is deep learning that accounts for uncertain...
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DistanceGeometric Graph Convolutional Network (DGGCN)
The distancegeometric graph representation adopts a unified scheme (dis...
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Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on ThreeDimensional (3D) Graphs
The geometry of threedimensional (3D) graphs, consisting of nodes and e...
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Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax
Deep learning models are full of hyperparameters, which are set manually...
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Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Classification
Tiered graph autoencoders provide the architecture and mechanisms for le...
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Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs
Tiered latent representations and latent spaces for molecular graphs pro...
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Probabilistic Generative Deep Learning for Molecular Design
Probabilistic generative deep learning for molecular design involves the...
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Latent Variable Modeling for Generative Concept Representations and Deep Generative Models
Latent representations are the essence of deep generative models and det...
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ConceptOriented Deep Learning: Generative Concept Representations
Generative concept representations have three major advantages over disc...
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ConceptOriented Deep Learning
Concepts are the foundation of human deep learning, understanding, and k...
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Daniel T Chang
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