1 citations · 1 across the 4 of their papers we have counts for
4 papers
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification
Shivvrat Arya, Yu Xiang, Vibhav Gogate
We present a unified framework called deep dependency networks (DDNs) that combines dependency networks and deep learning architectures for multi-label classification, with a parti…
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models
Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
We propose a self-supervised learning approach for solving the following constrained optimization task in log-linear models or Markov networks. Let and be two log-linear mo…
Neural Network Approximators for Marginal MAP in Probabilistic Circuits
Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
Probabilistic circuits (PCs) such as sum-product networks efficiently represent large multi-variate probability distributions. They are preferred in practice over other probabilist…
Deep Dependency Networks for Multi-Label Classification
Shivvrat Arya, Yu Xiang, Vibhav Gogate
We propose a simple approach which combines the strengths of probabilistic graphical models and deep learning architectures for solving the multi-label classification task, focusin…