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Probabilistic Discriminative Learning with Layered Graphical Models by Shen, Yuesong; Wu, Tao; Domokos, Csaba; Cremers, Daniel is a scholarly article available to read on EtoBox.

What is Probabilistic Discriminative Learning with Layered Graphical Models about?

Probabilistic graphical models are traditionally known for their successes in generative modeling. In this work, we advocate layered graphical models (LGMs) for probabilistic discriminative learning. To this end, we design LGMs in close analogy to neural networks (NNs), that is, they have deep hierarchical structures and convolutional or local connections between layers. Equipped with tensorized truncated variational inference, our LGMs can be efficiently trained via backpropagation on mainstream deep learning frameworks such as PyTorch. To deal with continuous valued inputs, we use a simple yet effective soft-clamping strategy for efficient inference. Through extensive experiments on image classification over MNIST and FashionMNIST datasets, we demonstrate that LGMs are capable of achieving competitive results comparable to NNs of similar architectures, while preserving transparent probabilistic modeling.

Author

Shen, Yuesong; Wu, Tao; Domokos, Csaba; Cremers, Daniel

Published

2019

Language

EN